Calorimetry in Food Processing Analysis and Design of Food Systems Institute of Food Technologists Series

Calorimetry in Food Processing:

Analysis and Design of Food Systems

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A John Wiley & Sons, Inc., Publication



EDITOR

Gönül Kaletunç

A John Wiley & Sons, Inc., Publication

Edition fi rst published 2009© 2009 Wiley-Blackwell and the Institute of Food Technologists

Chapter 7 remains with the U.S. Government.

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Library of Congress Cataloging-in-Publication Data

Calorimetry in food processing : analysis and design of food systems/editor Gönül Kaletunç. p. cm. Includes bibliographical references and index. ISBN-13: 978-0-8138-1483-4 (alk. paper) ISBN-10: 0-8138-1483-9 (alk. paper) 1. Food–Analysis. 2. Thermal analysis. 3. Calorimetry–Industrial

applications. 4. Food industry and trade. I. Kaletunç, Gönül TX544.C35 2009 338.4'7664–dc22

2009008348

A catalog record for this book is available from the U.S. Library of Congress.

Set in 11.5 on 13.5 pt Times by SNP Best-set Typesetter Ltd., Hong KongPrinted in Singapore

1 2009

www.wiley.com/wiley-blackwell

Titles in the IFT Press series

• Accelerating New Food Product Design and Development (Jacqueline H. Beckley, Elizabeth J. Topp, M. Michele Foley, J.C. Huang, and Witoon Prinyawiwatkul)

• Advances in Dairy Ingredients (Geoffrey W. Smithers and Mary Ann Augustin) • Biofi lms in the Food Environment (Hans P. Blaschek, Hua H. Wang, and Meredith E. Agle) • Calorimetry and Food Process Design (G ö n ü l Kaletun ç ) • Nondigestible Carbohydrates and Digestive Health (Teresa M. Paeschke and William R.

Aimutis) • Food Ingredients for the Global Market (Yao - Wen Huang and Claire L. Kruger) • Food Irradiation Research and Technology (Christopher H. Sommers and Xuetong Fan) • Food Laws, Regulations and Labeling (Joseph D. Eifert) • Food Risk and Crisis Communication (Anthony O. Flood and Christine M. Bruhn) • Foodborne Pathogens in the Food Processing Environment: Sources, Detection and Control

(Sadhana Ravishankar and Vijay K. Juneja) • Functional Proteins and Peptides (Yoshinori Mine, Richard K. Owusu - Apenten, and Bo

Jiang) • High Pressure Processing of Foods (Christopher J. Doona and Florence E. Feeherry) • Hydrocolloids in Food Processing (Thomas R. Laaman) • Microbial Safety of Fresh Produce (Xuetong Fan, Brendan A. Niemira, Christopher J.

Doona, Florence E. Feeherry, and Robert B. Gravani) • Microbiology and Technology of Fermented Foods (Robert W. Hutkins) • Multiphysics Simulation of Emerging Food Processing Technologies (Kai Knoerzer, Pablo

Juliano, Peter Roupas, and Cornelis Versteeg) • Multivariate and Probabilistic Analyses of Sensory Science Problems (Jean - Fran ç ois

Meullenet, Rui Xiong, and Christopher J. Findlay) • Nondestructive Testing of Food Quality (Joseph Irudayaraj and Christoph Reh) • Nanoscience and Nanotechnology in Food Systems (Hongda Chen) • Nonthermal Processing Technologies for Food (Howard Q. Zhang, Gustavo V. Barbosa -

C à novas, and V.M. Balasubramaniam, Editors; C. Patrick Dunne, Daniel F. Farkas, and James T.C. Yuan, Associate Editors)

• Nutraceuticals, Glycemic Health and Type 2 Diabetes (Vijai K. Pasupuleti and James W. Anderson)

• Packaging for Nonthermal Processing of Food (J. H. Han) • Preharvest and Postharvest Food Safety: Contemporary Issues and Future Directions (Ross

C. Beier, Suresh D. Pillai, and Timothy D. Phillips, Editors; Richard L. Ziprin, Associate Editor)

• Processing and Nutrition of Fats and Oils (Ernesto M. Hernandez and Afaf Kamal - Eldin) • Processing Organic Foods for the Global Market (Gwendolyn V. Wyard, Anne Plotto,

Jessica Walden, and Kathryn Schuett) • Regulation of Functional Foods and Nutraceuticals: A Global Perspective (Clare M. Hasler) • Sensory and Consumer Research in Food Product Design and Development (Howard R.

Moskowitz, Jacqueline H. Beckley, and Anna V.A. Resurreccion) • Sustainability in the Food Industry (Cheryl J. Baldwin) • Water Activity in Foods: Fundamentals and Applications (Gustavo V. Barbosa - C à novas,

Anthony J. Fontana Jr., Shelly J. Schmidt and Theodore P. Labuza) • Whey Processing, Functionality and Health Benefi ts (Charles I. Onwulata and Peter J. Huth)

vii

Dedication

For my parents, my son, and my husband for their patience and encouragement.

Hayatta en hakiki m ü r s it ilimdir. “ The truest guide in life is science. ”

— Mustafa Kemal Atat ü rk, September 22, 1924

This book is also dedicated to the memory of the late Professor Michel Ollivon, a great scientist and an exceptional human being, who passed away on June 16th, 2007, during the preparation of the book.

Dedication

viii

ix

Table of Contents

Preface xiiiContributor List xvii

Part 1 Analysis of Food and Biological Materials by Calorimetry 3

Chapter 1 Calorimetric Methods as Applied to Food: An Overview 5

Gönül Kaletunç

Chapter 2 Methods and Applications of Microcalorimetry in Food 15

Pierre Le Parlouër and Luc Benoist

Chapter 3 High-Pressure Differential Scanning Calorimetry 51

Günther W.H. Höhne and Gönül Kaletunç

Chapter 4 Calorimetry of Proteins in Dilute Solution 67 G. Eric Plum

Chapter 5 Thermal Analysis of Denaturation and Aggregation of Proteins and Protein Interactions in a Real Food System 87

Valerij Y. Grinberg, Tatiana V. Burova, and Vladimir B. Tolstoguzov

x Table of Contents

Chapter 6 Heat-Induced Phase Transformations of Protein Solutions and Fat Droplets in Oil-in-Water Emulsions: A Thermodynamic and Kinetic Study 119

Perla Relkin

Chapter 7 Analysis of Foodborne Bacteria by Differential Scanning Calorimetry 147

Michael H. Tunick, John S. Novak, Darrell O. Bayles, Jaesung Lee, and Gönül Kaletunç

Chapter 8 Coupling of Differential Scanning Calorimetry and X-Ray Diffraction to Study the Crystallization Properties and Polymorphism of Triacyglycerols 169

Christelle Lopez, Daniel J.E. Kalnin, and Michel R. Ollivon

Part 2 Calorimetry as a Tool for Process Design 199

Chapter 9 Overview of Calorimetry as a Tool for Effi cient and Safe Food-Processing Design 201

Alois Raemy, Corinne Appolonia Nouzille, Pierre Lambelet, and Alejandro Marabi

Chapter 10 Shelf Life Prediction of Complex Food Systems by Quantitative Interpretation of Isothermal Calorimetric Data 237

Simon Gaisford, Michael A.A. O’Neill, and Anthony E. Beezer

Chapter 11 Use of Thermal Analysis to Design and Monitor Cereal Processing 265

Alberto Schiraldi, Dimitrios Fessas, and Marco Signorelli

Chapter 12 Importance of Calorimetry in Understanding Food Dehydration and Stability 289

Yrjö H. Roos

Table of Contents xi

Chapter 13 High-Pressure Calorimetry and Transitiometry 311 Stanislaw L. Randzio and Alain Le Bail

Chapter 14 Calorimetric Analysis of Starch Gelatinization by High-Pressure Processing 341

Kelley Lowe and Gönül Kaletunç

Chapter 15 Use of Calorimetry to Evaluate Safety of Processing 351

Hans Fierz

Index 369

Preface

xiii

The global food industry is very large, producing sales worldwide on the order of approximately U.S. $1 trillion. To remain competitive in this complex industry, it is vital that manufacturers optimize food - processing conditions, most importantly not only to ensure the safety of food products but also to produce affordable, healthy, and conve-nient products, with desired sensory attributes. The global scale of the food industry brings the new challenges of increasing transport and export and in turn new requirements for increased shelf life. Optimization of food - processing conditions as well as development of new products requires knowledge of the physical properties of the food products and their components as the variables that are relevant to processing and storage conditions. Detailed knowledge of physical properties enables manufacturers to prevent waste of time and resources caused by trial and error during product formulation and process design.

Many food - processing protocols involve application of heating or cooling over a broad range of temperature. Knowledge of a food ’ s thermal properties as a function of temperature and composition is necessary for heat transfer and energy balance calculations used to rationally design these thermal - processing protocols. During process-ing, the food components go through conformational and phase changes that affect the state and texture of the fi nal food product.

Temperature - scanning calorimetry provides a useful tool for detecting, monitoring, and characterizing thermal processes in food materials. Moreover, calorimetry can be used to evaluate the effects of various physical and chemical stresses, including nonthermal treat-ments, on specifi c components by comparing the thermal profi les of pre - and post - treated food and biological materials to develop an

xiv Preface

understanding of the mechanism of processing - induced changes. The data generated from thermal analysis techniques also can be used to develop equations that predict the physical properties of pre - and post-processed foods as a function of processing and storage conditions.

Although the use of calorimetry to measure the physical properties of food materials has increased both in academia and in industry over the past 20 years, the analysis of data frequently is complicated by multiple overlapping transitions and kinetically controlled events that occur in food materials.

This book is designed to introduce the basic principles of calori-metry, applications of calorimetry to characterize food products, inter-pretation of the resultant data, and the use of these data for process optimization and product development. The book is organized in two sections. The fi rst section, consisting of eight chapters, focuses on the basic principles of calorimetry and its use for a wide range of materials from dilute solutions to solids. The second section, consisting of seven chapters, emphasizes the use of calorimetric data as a tool for process design and product development.

Chapter 1 provides an overview of calorimetry and the organization of the book. Chapters 2 and 3 focus on experimental design principles, calibration, data collection and analysis for microcalorimetry and high - pressure calorimetry. Chapter 4 addresses applications of ultrasensitive calorimetry to proteins and their interactions in dilute solution to char-acterize the thermal and thermodynamic stability and the thermody-namic origins of that stability. Chapters 5 , 6 , and 7 undertake the characterization of concentrated, multicomponent systems that are commonly observed in foods and complex biological systems such as bacteria. The fi nal chapter in this section, Chapter 8 , focuses on the use of an instrument that combines X - ray diffraction and high - sensitivity differential scanning calorimetry (DSC) in the same appa-ratus to simultaneously obtain complementary thermal and structural information for a sample.

Section Two of the book comprises Chapters 9 through 15 . Chapter 9 provides an overview of the use of phase transition information in development of phase diagrams that can be used for effi cient process design. Chapter 10 covers application of isothermal calorimetry for analysis of food stability, shelf life, and isothermal cooking processes. Chapter 11 describes application of thermal analysis to cereal - based products and mathematical treatment of the complex thermograms to

Preface xv

deconvolute the contributions from different components of the system. Chapter 12 reviews the use of calorimetric data for selection of dehy-dration parameters to produce products with improved storage stabil-ity. Chapter 13 describes the relatively new technique of scanning transitiometry and its specifi c application to gelatinization of wheat starch dispersions and for investigation of pressure shift freezing. Chapter 14 covers the application of calorimetry to characterize the impact of nonthermal treatment and to determine kinetic parameters during storage. Chapter 15 reviews the use of calorimetry to quantify the probability and potential severity of exothermic events such as formation of hot spots in dryers and to establish safe conditions for handling materials to prevent accidents in the food industry.

This book is designed to explain the capabilities of calorimetry for characterization of food and biological systems, which can range from single component, single - phase systems to multicomponent, multi-phase systems. Therefore, information described in the book will provide comprehensive insight for scientists who have experience with calorimetry as well as a basic understanding for beginners. This text may also be used as a textbook for a graduate - level course. The book is also intended to serve as a resource for food scientists, food tech-nologists, and food engineers working in the area of process design, optimization, and product development. The descriptions of the basic principles and potential uses of calorimetry to provide critical informa-tion for their respective areas and will serve as a bridge between these workers and specialists in calorimetry.

xvii

Contributors

Bayles, Darrell O. (Chapter 7 ) Dairy Processing & Products Research Unit, USDA - ARS - Eastern Regional Research Center, Wyndmoor, PA, USA

Beezer, Anthony E. (Chapter 10 ) The School of Pharmacy, University of London, London, UK

Benoist, Luc (Chapter 2 ) SETARAM, Lyon, France

Burova, Tatiana V. (Chapter 5 ) Nesmeyanov Institute of Organo - Element Compounds, Russian Academy of Sciences, Moscow, Russian Federation

Fessas, Dimitrios (Chapter 11 ) DISTAM, University of Milan, Milano, Italy

Fierz, Hans (Chapter 15 ) Swiss Safety Institute, Basel, Switzerland

Gaisford, Simon (Chapter 10 ) The School of Pharmacy, University of London, London, UK

Grinberg, Valerij Y. (Chapter 5 ) A.N. Nesmeyanov Institute of Organo - Element Compounds, Russian Academy of Sciences, Moscow, Russian Federation

xviii Contributors

H ö hne, G ü nther W.H. (Chapter 3 ) University of Ulm, Ulm, Germany (Retired)

Kaletun ç , G ö n ü l (Chapters 1, 3, 7, 14) The Ohio State University, Department of Food Agricultural and Biological Engineering, Columbus, OH, USA

Kalnin, Daniel J.E. (Chapter 8 ) YKI, Ytkemiska Institutet AB, The Institute for Surface Chemistry, Stockholm, Sweden

Lambelet, Pierre (Chapter 9 ) Nestl é Research Center, Nestec LTD, Lausanne, Switzerland

Le Bail, Alain (Chapter 13 ) ENITIAA, UMR CNRS GEPEA (6144), Nantes, France

Lee, Jaesung (Chapter 7 ) Department of Food Science and Technology, The Ohio State University, Columbus, OH, USA

Le Parlou ë r, Pierre (Chapter 2 ) Thermal Consulting, Caluire, France

Lopez, Christelle (Chapter 8 ) UMR Science et Technologie du Lait et de l ’ Oeuf, INRA - Agrocampus Ouest, Rennes Cedex, France

Lowe, Kelley (Chapter 14 ) Abbott Nutrition Products Division, Columbus, OH, USA

Marabi, Alejandro (Chapter 9 ) Nestl é Research Center, Nestec Ltd., Lausanne, Switzerland

Nouzille, Corinne Appolonia (Chapter 9 ) Nestl é Research Center, Nestec Ltd., Lausanne, Switzerland

Novak, John S. (Chapter 7 ) Dairy Processing & Products Research Unit, USDA - ARS - Eastern Regional Research Center, Wyndmoor, PA, USA

Contributors xix

Michel Ollivon (Chapter 8 , published posthumously) Universit é Paris - Sud, Chatenay - Malabry, France

O ’ Neill, Michael A.A. (Chapter 10 ) Department of Pharmacy and Pharmacology, University of Bath, Bath, UK

Plum, G. Eric (Chapter 4 ) IBET Inc., Columbus, OH, USA, and Rutgers, The State University of New Jersey, Department of Chemistry and Chemical Biology, Piscataway, NJ, USA

Raemy, Alois (Chapter 9 ) Nestl é Research Center, Nestec Ltd., Lausanne, Switzerland

Randzio, Stanislaw L. (Chapter 13 ) Polish Academy of Sciences, Institute of Physical Chemistry, Warszawa, Poland

Relkin, Perla (Chapter 6 ) UMR 1145 (AgroParisTech, CEMAGREF, INRA), AgroParisTech, Department of Science and Engineering for Food and Bioproducts, Massy, France

Roos, Yrj ö H. (Chapter 12 ) Department of Food and Nutritional Sciences, University College Cork, Ireland

Schiraldi, Alberto (Chapter 11 ) DISTAM, University of Milan, Milano, Italy

Signorelli, Marco (Chapter 11 ) DISTAM, University of Milan, Milano, Italy

Tolstoguzov, V.B. (Chapter 5 ) Tolstoguzov consulting.com, Pully, Switzerland.

Tunick, Michael H. (Chapter 7 ) Dairy Processing & Products Research Unit, USDA - ARS - Eastern Regional Research Center, Wyndmoor, PA, USA



Analysis of Food and Biological Materials by Calorimetry

Part 1

Chapter 1

Calorimetric Methods as Applied to Food: An Overview

G ö n ü l Kaletun ç

Introduction 5 Calorimetry 6 An Overview of the Book 8 References 13

Introduction

Several thermal and nonthermal methods are applied to process and preserve food materials and to manufacture value - added products. The goals of food processing are to inactivate spoilage and pathogenic microorganisms and to maintain this status in storage during the intended shelf life of the product. During processing, changes take place in food components, including vitamins, lipids, carbohydrates, and proteins. Such changes lead to structural and functional changes in foods at the micro - and macromolecular levels that affect the physi-cal, organoleptic, and nutritional properties of the food.

Food materials are complex biological systems. Food products may have a broad range of structures spanning the three states of matter, including dilute to concentrated liquids, solids, and mixtures of multi-liquid, liquid - solid, liquid - gas, and solid - gas structures. The combina-tion of complex structures making up complex biological compounds makes the characterization of food systems challenging. To address the wide variety of compositions and structures, many biophysical techniques are uesd to characterize the structure and properties of food materials before and after processing to develop a fundamental

5

6 Calorimetry in Food Processing

understanding of the impact of processing and storage conditions. The data resulting from such studies can be used to predict the physical prop-erties of foods so that food processing and storage conditions are optimized.

Calorimetry

Among biophysical techniques, calorimetry presents itself as particu-larly well suited for analysis of food materials. Among many reasons, the fi rst is the relevance of the experimental protocols of calorimetry to the majority of processes employed in food preservation. Specifi -cally, because many food - processing methods involve thermal treat-ment (heating, cooling, freezing) of the materials, thermal characterization of food systems and their components leads to data that can be related directly to the processing protocols. Determination of thermal properties of food materials, such as specifi c heat as a func-tion of temperature, is essential for heat transfer and energy balance calculations (Kaletun ç 2007 ). Generation of a reliable database to develop equations predicting thermal properties of food materials for optimization of food processes can be accomplished by using calorim-etry. Moreover, food materials and their components go through con-formational and phase transitions during processing. Calorimetry data can be analyzed to evaluate the thermal and thermodynamic stability of various phases for a rational design of food product formulations and process conditions.

Differential scanning calorimetry, which measures heat capacity as a function of temperature, is a well - established thermal analysis technique that detects and monitors thermally induced conformational transitions and phase transitions as a function of temperature. During temperature scanning, depending on the complexity of the material, many peaks or infl ection points (one to several) refl ecting the thermally induced transitions can be observed. The direction of the peak corresponds to the nature of the transition, being heat absorbing (endo-therms) or heat releasing (exotherms). While melting of solids and denaturation of proteins display endotherms, crystallization of carbo-hydrates and aggregation of proteins manifest themselves as exotherms. The temperatures for the endothermic and exothermic transitions and

Calorimetric Methods as Applied to Food: An Overview 7

the heat involved in such transitions are measured using a calorim-eter. Infl ection points are indicative of glass transitions; that is, transitions from a glassy to rubbery state. The transition temperatures ( T peak or T g ) refl ect the thermal stability of the phase or state going through the transition. One can extract from calorimetry data values for the thermal and thermodynamic changes in free energy ( Δ G ), enthalpy ( Δ H ), entropy ( Δ S ), and heat capacity ( Δ C p ) of the various transitions in addition to determination of the bulk heat capacity of the material.

The basis for thermodynamic study of food materials is that the relevant initial and fi nal states (preprocessing and postprocessing states) can be defi ned and the energetic and structural differences between these states can be measured using calorimetric instrumenta-tion. To this end, calorimetry can be used to evaluate the effect of other physical and chemical variables by comparing the thermograms of the materials before and after exposure to the variable outside the calorimetry.

The basics of application of calorimetry to food materials are dis-cussed in detail in this book. However, it is important to start the discussion with a summary of the advantages of using calorimetry for study of biological materials. These advantages can be outlined as follows:

• Direct measurement of the energetics of the transition is obtained ( Δ H and Δ C p ). The experimental results are not model dependent.

• Calorimetry can be applied to a range of materials, pure or complex. Materials do not have to be optically transparent or have chromo-phores as required by spectroscopic methods.

• Materials do not have to be uniform or have to be a homogeneous mixture. In fact, in addition to pure materials, the technique can be used to evaluate the interactions among the components in a complex system and how the interactions are altered by the processing.

• Calorimetry does not require elaborate or destructive sample preparation.

• Calorimetry is an established technique which has been around since the 16th century (Haines 1995 ). Today, the instruments are highly developed for accurate measurement of thermal events. The theory behind the technique is well developed, which facilitates interpreta-tion of the data (H ö hne et al. 2003 ).


While the technique is powerful, the validity and utility of the data depend strongly on the careful use of the equipment and correct inter-pretation of data. Some analytical methods provide results specifi c to materials; however, calorimetry data depends on the conditions used during the experiment (Haines 1995 ). One must be careful in choosing the calorimetry parameters:

1. Time scale: Especially in dynamic measurement systems, for events to be detected the experimental time scale should match the time scale of the observed event.

2. Magnitude of the heat fl ow: If the energy associated with the transi-tion is small, it can lead to ambiguities in its detection. Increasing the scanning rate enhances the signal; however, it may cause devia-tion from equilibrium conditions, which requires models beyond the standard equilibrium thermodynamics treatment of calorimetry data.

3. Moisture loss during experiment: Biological samples in general are high - moisture content materials. If the sample cell is not sealed well, the moisture content of the sample will change due to evapora-tion during the course of experiment. This may lead to overestima-tion of the transition temperature as well as the transition enthalpy change.

4. Interpretation of overlapping peaks: Biological samples may contain multiple components that undergo thermally induced transitions at similar temperatures. As a result, overlapping peaks may be observed on a differential scanning calorimetry (DSC) thermogram. Even if the origin of the event is known, because the peak temperatures may shift due to overlap, individual events may appear to happen at dif-ferent temperatures. The individual peaks can be resolved experi-mentally (Barrett et al. 2002, 2005 ), or the complex thermograms can be deconvoluted by using special software (Fessas and Schiraldi 2000 ).

An Overview of the Book

This book focuses on the basics of calorimetry and specifi c applica-tions for characterization of food systems. The material in this book is designed to provide food scientists, food technologists, and food engi-


neers with knowledge about the potential uses of calorimetry as a tool in process design and optimization as well as product development and improvement. The book consists of two sections. The fi rst section includes eight chapters describing the principles of calorimetry alone and coupled with other techniques as well as the use of calorimetry to characterize biological systems ranging from pure single phase to multicomponent and multiphase systems of solids, dilute and concen-trated solutions of macromolecules, emulsions, foams, and bacteria. The second section of the book is designed to illustrate the use of calorimetric data to guide engineers and processors in design and opti-mization of processes.

The multicomponent nature of the food materials presents a chal-lenge in that the specifi c component undergoing a conformational or phase transition may be in small quantity relative to the whole, thus generating an insuffi cient heat signal to detect. As an alternative to increasing the heating rate, the heat signal can be enhanced by increas-ing the sample size. In Chapter 2 , the challenges of increasing sample size and strategies to overcome these challenges by using micro-calorimetry are discussed.

The increased interest of consumers in minimally processed foods pushed the food research community to explore novel technologies that present alternatives to thermal processing. High hydrostatic pressure (HHP) processing has become the most promising alternative technol-ogy. Currently, HHP processing is implemented for several foods and has a market value of more than $500 million. The optimization of HHP processing requires knowledge of physical properties under con-ditions relevant to the pressures attained during the process. The design of calorimeters operating under the pressures used in industry is very challenging. Chapter 3 focuses on the design of a high - pressure calo-rimeter and the protocols to be followed for calibration, data collection, and analysis.

Applications of ultrasensitive calorimetry to proteins and their inter-actions in dilute solution are examined in Chapter 4 . Emphasis is placed on the practical aspects of collecting and analyzing differential scanning calorimetry (DSC) data to characterize the thermal and ther-modynamic stability and the thermodynamic origins of that stability in a protein in solution. The thermodynamics of association between a protein and a small molecule or another macromolecule are quantifi -able by application of isothermal titration calorimtery (ITC). The


design and execution of ITC experiments are described with emphasis on the information content of titration curves. Together or separately, DSC and ITC provide valuable tools for developing a predictive under-standing of protein stability and interactions as a function of tempera-ture and solution conditions.

Investigation of dilute systems is essential to elucidate the behavior of macromolecules thermodynamically. However, in biological systems and foods, dilute systems are rarely encountered. Commonly, macro-molecules exist in foods at high concentration and in complexes with other macromolecules and low - molecular - weight compounds. Heat denaturation and aggregation of proteins are common during food processing and affect the quality attributes of food. Therefore, Chapter 5 uses calorimetry to study the effects of pH, salts, alcohols, and poly-saccharides on thermal denaturation and aggregation of food proteins in order to elucidate the mechanisms of structure formation, structure - texture and structure - physical property relationships in foods.

Proteins also play an important role in development of emulsions and foams that are examples of multicomponent and multiphase food systems. Both the formation and the stability of such complex systems depends on the adsorption properties of proteins at oil - in - water or gas - in - water interfaces. Chapter 6 reviews the use of DSC in scanning and isothermal mode for monitoring effects of food composition and physi-cochemical environment on the conformation and structural modifi ca-tions of proteins in emulsions under the time - temperature combinations relevant to processing. The results presented in this chapter illustrates that a combination of thermodynamic and kinetic data obtained by using DSC in scanning and isothermal modes provide a better under-standing of emulsions and the ability to control structure - forming mechanisms in food systems.

The main goal of food processing is to manufacture foods that are stable and safe to consume, which requires the inactivation of bacteria to prevent spoilage and foodborne diseases. Thermal inactivation of microorganisms is associated with irreversible denaturation of mem-branes, ribosomes, proteins, and nucleic acids. DSC can be used to monitor the reversible and irreversible changes in the cellular compo-nents of bacteria. Chapter 7 describes using DSC to provide an insight into the mechanism of bacterial cell inactivation. Also illustrated is the utility of DSC data to quantitatively evaluate bacterial inactivation kinetics. Calorimetry can be used to evaluate the effect of food -


processing variables other than heat on bacteria. Chapter 7 describes the analysis by calorimetry of damage to bacterial cells due to chemi-cal, nonthermal, or antibiotic treatments and the relationship between the calorimetric data and loss of cell viability.

The data collected by calorimetry are complementary to data col-lected by other biophysical methods. Thermal analysis is a valuable tool to observe phase transition, but especially for complex systems, such as lipids, the thermal observables can be due to a variety of struc-tures forming during the heating or cooling process. Generally, another technique such as Fourier transform infrared spectroscopy or x - ray diffraction (XRD) is used in parallel to acquire structural information. Obtaining complementary data can be further improved by performing simultaneous DSC - FTIR (Yoshida 1999 ) or DSC - XRD (Yoshida et al. 1996 ; Ollivon et al. 2006 ) measurements on the same sample. Chapter 8 describes in detail the development of a new instrument, called MICROCALIX, combining XRD at both wide and small angles as a function of temperature (XRDT) or time (XRDt), and high - sensitivity DSC, in the same apparatus with scanning or isothermal modes over the temperature range − 30 to +230 ° C. This approach enables one to obtain complementary thermal and structural properties information on the same sample in one experiment.

Foods exhibit thermally induced transitions over a temperature range between − 50 ° C and 300 ° C. The thermal behavior of a food is mainly a refl ection of its major component, however, with some change due to interactions with other components. Chapter 9 focuses on the use of phase transition information in development of phase diagrams that can be used for effi cient process design. Heat of a solution as a parameter of great importance for food powder dissolution is also emphasized. The relevance of calorimetric data to the food industry is illustrated by specifi c examples.

Biological samples undergo changes even when they are kept at constant temperature. Changes, physical or chemical in origin, may produce heat that can be studied with isothermal calorimetry. However, detection and monitoring of small quantities of heat, especially at the initial stage of the physical or chemical event, requires using a high - sensitivity calorimeter. Chapter 10 focuses on application of isother-mal calorimetry, a relatively less - exploited application of calorimetry in comparison with DSC, for qualitative and quantitative analysis of food stability, shelf life, and isothermal cooking processes. Specifi c


examples are discussed, from simple ingredients to complex biological processes.

Cereal - based products are staple foods all around the world. Although the main component in such foods is starch, thermally induced transitions are highly affected by the presence of other com-pounds in cereals, including proteins, nonstarch carbohydrates, and lipids, either due to competition for available water or direct interac-tions. Chapter 11 provides a review of thermal analysis applications to cereal - based products and cereal processing. This chapter discusses in detail mathematical treatment of the complex thermograms to decon-volute the contributions from different components in the system.

Drying has been used as a method of food preservation since ancient times. In modern practice, water is removed by evaporation upon application of heat or by sublimation from a frozen product under vacuum. During the drying process, amorphous or partially crystalline states are formed. The thermal stability of the amorphous state is defi ned by the glass transition temperature, which depends strongly on the amount of water present in the food system. Chapter 12 reviews the use of calorimetric data for selection of dehydration parameters to produce products with improved storage stability. This chapter also discusses the relationship between the glass transition and collapse of structure in freeze - dried materials, fl avor retention by encapsulation of volatiles in amorphous systems, solids crystallization, lipid oxidation, nonenzymatic browning, and enzymatic changes.

Chapter 13 describes the relatively new technique of scanning tran-sitiometry developed by Randzio (1996) based on scanning of one of the three variables — pressure, volume, or temperature — and measure-ment of the other two, as well as the heat signal. This chapter also discusses the specifi c application of scanning transitiometry for gela-tinization of wheat starch dispersions and for investigation of pressure shift freezing. In addition, the technique is applied to the study of water, water in pork muscle, solutions of gelatin in water, and lipids.

Chapter 14 focuses on the application of calorimetry to determine the effects of high hydrostatic pressure on starch gelatinization as well as to characterize the recrystallization of the gelatinized starch during subsequent storage for calculation of starch recrystallization kinetic parameters. These results are used in selection and optimization of HHP processing parameters and storage conditions for foods contain-ing starch.


Foods show chemical reactivity leading to self - heating and self - ignition of hot spots. Especially handling of dry powders in bulk, such as in milling, drying, and packaging, can be dangerous due to potential dust explosions. Chapter 15 reviews the evaluation by calorimetry of the thermal consequences of exothermic decompositions in foods, describes the methodology for quantifying the risk in terms of its sever-ity and its probability, and discusses methods for collecting the stabil-ity data correctly. Specifi c cases of formation of hot spots in dryers, storage and hot discharge, and transport safety are discussed. The importance of establishing safe conditions for handling of materials in prevention of accidents in the food industry is emphasized.

References

Barrett A. , Cardello A. , Maguire P. , Richardson M. , Kaletun ç G. , and Lesher L. 2002 . Effects of Sucrose Ester, Dough Conditioner, and Storage Temperature on Long - Term Textural Stability of Shelf - Stable Bread . Cereal Chem , 79 ( 6 ): 806 – 811 .

Barrett A.H. , Marando G. , Leung H. , and Kaletun ç G. 2005 . Effect of Different Enzymes on the Textural Stability of Shelf - stable Bread . Cereal Chem , 82 ( 2 ): 152 – 157 .

Fessas D. , and Schiraldi A. 2000 . Starch Gelatinization Kinetics in Bread Dough, DSC Investigations on Simulated Baking Processes . J Therm Anal Calorim , 61 : 411 – 423 .

Haines P.J. 1995 . Thermal Methods of Analysis, Principles, Applications and Problems . Glasgow : Blackie .

H ö hne G.W.H , Hemminger , W. , and Flammersheim , H.J. Differential Scanning Calorimetry: an Introduction for Practitioners . 2nd Ed. Berlin; New York : Springer - Verlag , 2003.

Kaletun ç G. 2007 . Prediction of Heat Capacity of Cereal Flours: A Quantitative Empirical Correlation . J Food Eng , 82 ( 2 ): 589 – 594 .

Ollivon M. , Keller G. , Bourgaux C. , Kalnin D. , Villeneuve P. , and Lesieur P. 2006 . DSC and High Resolution X - Ray Diffraction Coupling . J Therm Anal Calorim , 85 : 219 – 224 .

Randzio S.L. 1996 . Scanning Transitiometry . Chemical Society Reviews , 25 : 383 . Yoshida H. , Ichimura Y. , Kinoshita R. , and Teramoto Y. 1996 . Kinetic Analysis of

the Isothermal Crystallization of an N - Alkane and Polyethylene Observed by Simultaneous DSC/FT - IR/WAXD Measurement . Thermochim Acta , 282/ 283 : 443 – 452 .

Yoshida H. 1999 . Structure Relaxation of N - Alkanes Observed by the Simultaneous DSC/FTIR Method. J Therm Anal Calorim , 57 ( 3 ): 679 – 685 .

Chapter 2

Methods and Applications of Microcalorimetry in Food

Pierre Le Parlou ë r and Luc Benoist

15

Introduction 15 The Heat Flux Calorimetric Principle 17 DSC versus Heat Flux Microcalorimetry 19

Comparison between DSC and Heat Flux Microcalorimetry 19 The Calvet Principle 22 Calibration 23

Description of Different Heat Flux Calorimeters Used for Food Characterization 26

High Sensitivity Heat Flux Calorimeter 26 The Mixing and Reaction Heat Flux Microcalorimeter 29

Methods of Microcalorimetry in Food 30 Heat Capacity Determination 30 Heating Mode 35 Mixing and Reaction Calorimetry 40 Pressure Calorimetry 43 Calorimetry under Controlled Relative Humidity 45

Conclusion 45 References 46

Introduction

Heat is involved at different steps in the preparation of foods, such as cooking and processing. During heating, cooling, or freezing, the food products undergo different types of transformations, including melting,


crystallization, gelation, gelatinization, denaturation, and oxidation. All these transformations occur in a certain range of temperature and are associated with heat variations. The thermal analysis techniques, and specifi cally differential scanning calorimetry (DSC), are used as a main approach for investigating the thermal properties of foods (Harwalkar and Ma 1990 ; Farkas and Moh á csi - Farkas Csilla 1996 ; Schiraldi et al. 1999 ; Raemy et al. 2000 ).

However, in most food processing food ingredients are mixed or diluted with a liquid (water, milk) or with a powder (sugar, salt, yeast). For simulation of such transformations and interactions, the limited volume and the lack of in situ mixing constitute the major drawbacks of the DSC technique.

For such investigations, microcalorimetry (in the isothermal and scanning modes) is the ideal solution because it has the capacity to work on bulk materials and diluted solutions with a very high sensitivity. Microcalorimeters are found as reaction or solution calo-rimeters, pressure calorimeters according to the transformation to be simulated, and provide a wide range of experimental conditions for applications such as mixing, dilution, wetting, neutralization, and enzymatic reaction, which have relevance to food industry. For a food technologist, it is very important to understand various thermal and functional properties of food components and ingredients for fundamental research, food quality assurance, and for product development.

Although many articles have been published in the fi eld, to our knowledge a book dedicated to the very challenging fi eld of microca-lorimetric applications in food science has not been available.

Microcalorimetry, compared with DSC, still remains as a lesser - known technique. For a long time, it has suffered from a reputation as an old and slow technique (needing days of experimentation), of large instruments (microcalorimetry meaning microquantity of measured heat and not microsize instrument), and was used mainly by experts. Especially with the development of microcalorimetry in the biological and pharmaceutical fi elds (Ladbury and Chowdhry 2004 ; Craig and Reading 2007 ), there have been many advances in instrumentation in the last decade that facilitated the use of calorimeters in laboratories. Microcalorimetry benefi ts food research and opens new opportunities of experiments and applications that are to be described in this chapter.

Methods and Applications of Microcalorimetry in Food 17

The Heat Flux Calorimetric Principle

Existing calorimeters operate on the following principles:

• the heat fl ux principle • the heat - compensating principle • the heat - accumulating principle

In this chapter, the heat fl ux calorimetric principle is described as it is used in most calorimeters for food characterization.

The heat fl ux calorimeter consists of a measurement chamber sur-rounded by a detector (thermocouples, resistance wires, thermisters, thermopiles) to integrate the heat fl ux exchanged by the sample con-tained in an adapted vessel. The measurement chamber is insulated in a surrounding heat sink made of a high thermal conductivity material.

The heat fl ux for a given sample at a temperature T s is equivalent to:

dqdt

dhdt

C dTdt

ss

s= − +

(2.1)

where dh / dt is heat fl ux produced by the transformation of the sample or the reaction and C s is heat capacity of the sample, including the container. The heat fl ux dq s / dt is exchanged with the thermostatic block at a temperature T p through a thermal resistance, R , described by the following relation:

dqdt

T TR

s p s=−

(2.2)

Equation 2.1 shows that the thermal contribution due to the heat capac-ity of the sample and container is very large and will provide a major disturbance at the introduction of the container in the calorimeter. From Equation 2.2 , it is also evident that any temperature perturbation of the thermostatic block will affect the calorimetric measurement.

To solve these issues, a symmetrical calorimeter is preferred. Two identical calorimetric chambers, one housing a container with the sample and an identical reference container an inert material


(the reference container may also be empty) are placed in the thermo-static block at the same temperature, T p . The heat fl ux difference is measured between the two chambers.

dqdt

dqdt

dqdt

dhdt

C dTdt

C dTdt

s rs

sr

r= − = − + −

(2.3)

Here, C r is heat capacity of the reference, including the container, and T r is temperature of the reference.

Equation 2.2 becomes:

dqdt

T TR

r s= −

(2.4)

or by derivation

R d qdt

dTdt

dTdt

r s2

2 = −

(2.5)

By combining Equations 2.4 and 2.5 , the characteristic equation for the calorimetric measurement is obtained.

dhdt

dqdt

C CdTdt

RC d qdts r

ps= − + −( ) −

2

2

(2.6)

TsCs

Heat sink

RThermostatic block

Heating elements

Tp

Sample + container

Figure 2.1. One - cell calorimetric principle.


If dh / dt corresponds to an endothermic transformation or reaction, the dh / dt value is positive. If dh / dt corresponds to an exothermic trans-formation or reaction, the dh / dt value is negative.

If the calorimetry is performed isothermally, the parameter dT p / dt is null. In a small perturbation of the temperature T p of the thermostatic block, the corresponding thermal effect will be minimized if the C s and C r heat capacities are similar. The last term R C s d 2 q / dt 2 (called as thermal lag) mostly depends on the thermal resistance or the time of response of the calorimeter and the heat capacity of the sample and the container. For a long period ( t >> RC s ) it will be negligible. Table 2.1 gives an overview of some endothermic or exothermic effects occur-ring in various types of food.

DSC versus Heat Flux Microcalorimetry

Comparison between DSC and Heat Flux Microcalorimetry

The differences between DSC and heat fl ux microcalorimetry are related mainly to the size of the sample and the sensitivity of the mea-surement but also to interactions between solid and liquid materials. To clearly understand the difference, it is important to analyze the technological principles that are behind each technique.

Table 2.1. Some endothermic and exothermic effects for different food types.

Food Type Endothermic Effect Exothermic Effect

Fat, oil Melting, lipidic transition Crystallization, oxidation Protein Denaturation Aggregation,

crystallization Enzyme Denaturation Aggregation, enzymatic

reaction Starch Gelatinization, glass

transition Retrogradation, oxidation

Milk Melting Crystallization, oxidation Hydrocolloid, gelatin Melting Gelation Carbohydrates Melting, glass transition Crystallization,

decomposition Yeast Fermentation Bacteria Growth, metabolism,

fermentation


International Confederation for Thermal Analysis and Calorimetry (ICTAC), in its nomenclature, considers two types of DSC: the heat fl ux DSC and the power - compensated DSC ( www.ictac.org ). Even if the measurement principles are different, the heat transfer from (or to) the sample is about the same. The detector for each DSC model is a plate - type design. The sample, contained in a metallic crucible, is placed and centered on the plate acting as a fl at - shaped sensor. A refer-ence crucible (empty or containing an inert material) is placed on the other plate. In plate DSC (heat fl ux type and power - compensated type), the heat exchange between the sample and the detector occurs through the bottom of the crucible, corresponding to a two - dimensional detec-tion. In fact, only a part of this heat transfer is measured, as a signifi cant part is dissipated through the walls and the cover of the crucible (Figure 2.2 ). The ratio of the heat fl ux measured by the sensor to the total heat fl ux produced by the thermal event, calculated by simulation using

Figure 2.2. Schematic of a plate - shaped DSC sensor.


thermal modeling software, shows that only around half of the heat fl ux is dissipated through the plate (Daudon 1996 ; Le Parlou ë r and Mathonat 2005 ). Figure 2.3 clearly shows that the effi ciency rapidly decreases with the temperature and the thickness of the plate. The effi ciency is also affected by the amount of the sample tested. Therefore, it is recommended to work with small amount of material (about 5 – 10 mg) when using a plate DSC to minimize the heat losses. The thermal conductivities of the crucible and the gas used in the experi-mental chambers also are very important parameters to be considered in the effi ciency of the heat exchange. For example, a very heat - conductive gas (helium) will favor the heat transfer between the crucible and the detector, but at the same time increase the heat losses. Hence, the calibration of a plate - type DSC (heat fl ux or power - compensated type) is very critical and has to be run with the experi-mental conditions selected for testing the sample.

The main difference between heat fl ux calorimetry and DSC (heat fl ux or power compensated type) is that in a microcalorimeter, the heat exchange between the sample and the detector is completely measured. Such a high effi ciency is achieved by applying the technological prin-ciple developed by Tian and Calvet.

Calorimeters are also designed on the power - compensating prin-ciple using a detector that surrounds the sample in the same way. MicroCal ( www.microcal.com ) and CSC (now TA Instruments)

50

40%

Flu

x 30

20

10

00 150 300

Temperature (°C)

450 600

0.01 mm0.05 mm0.10 mm

Figure 2.3. Effi ciency ratio of a fl at - shaped DSC as a function of the sensor plate thickness.


( www.tainstruments.com ) have developed such ultrasensitive instru-ments, mostly used for the investigations of dilute liquids. Because these calorimeters operate with fi xed vessels, they are not well - adapted for the characterization of foods.

The Calvet Principle

The detection is based on a three - dimensional fl uxmeter sensor. The fl uxmeter element consists of a ring of several thermocouples in series (Figure 2.4 ). The corresponding thermopile of high thermal conductivity surrounds the experimental space within the calorimetric block. The radial arrangement of the thermopiles guarantees an almost complete integration of the heat. This is verifi ed by the calculation of the effi ciency ratio that indicates that an average value of 94% ± 1% of heat is transmitted through the sensor on the full range of tempera-ture of the Calvet - type DSC (Figure 2.5 ). In this setup, the sensitivity of the DSC is not affected by the type of crucible, the type of purge gas, or the fl ow rate. The main advantage of the setup is the increase

Figure 2.4. Schematic of the Calvet type calorimeter.


of the experimental vessel ’ s size, and consequently the size of the sample, without affecting the accuracy of the calorimetric measurement.

Calibration

The calibration of the calorimetric detectors is a key parameter and has to be performed very carefully. In fact, the main purpose of the calibra-tion is to transform the electric signal (emf) provided by the thermo-couples of the detector expressed in microvolts ( μ V) in a thermal power (heat fl ux) signal expressed in milliwatts (mW). For DSC detec-tors, this conversion is achieved using metallic reference materials (Richardson and Charsley 1998 ). Although this recommended proce-dure is widely used, it has some limitations:

• The calibration can only be performed at the temperature at which the reference material melts.

• At low temperature, it is diffi cult to fi nd good reference materials. • The calibration is mostly performed in a heating mode, but very

rarely in the cooling mode. • The accuracy of the calibration depends on the purity and quality of

the reference materials.

For Calvet - type calorimeters, a specifi c calibration, so - called Joule effect or electrical calibration, has been developed to overcome the drawbacks described above (Calvet and Prat 1964 ). A dedicated vessel

Ratio of energy transmitted through the thermopile

9393.293.493.693.8

9494.294.494.694.8

95

0 100 200 300 400 500

T (°C)

Ra

tio

of

en

erg

y (

%)

Figure 2.5. Effi ciency ratio of a Calvet - type calorimeter versus temperature.


with a built - in electrical heater (platinum resistance) simulating the experimental vessel that contains the sample is introduced into the calorimeter at a given temperature. A well - defi ned electrical power (between 20 and 200 mW) is applied to the resistance. The calorimeter gives a corresponding deviation (Figure 2.6 ). The stabilized signal, expressed in microvolts, is directly correlated to the applied power, expressed in milliwatts. The main advantages of this type of calibration are as follows:

• It is an absolute calibration. • The use of standard materials for calibration is not necessary. The

calibration can be performed at a constant temperature, in the heating mode and in the cooling mode.

• It can be applied to any experimental vessel volume. • It is a very accurate calibration.

To understand the direct correlation between the electrical signal and the heat fl ux, consider that a power, W , is fully dissipated in a calibra-tion vessel (Figure 2.7 ) surrounded by a fl uxmeter composed of crowns of thermocouples (Figure 2.3 ). An elementary power, w i , is dissipated through each thermocouple giving an elementary variation of tempera-ture Δ T i between the internal and external weldings:

w Ti i i= δ Δ (2.7)

where Δ i is the conductance of the thermocouple.

P

S

K=S/P

Figure 2.6. Joule effect calibration.


The corresponding variation of temperature generates an elementary electromotive force (emf) according to the Oersted law:

e Ti i i= ε Δ (2.8)

where ε i is the thermoelectric constant of the thermocouple. By combining Equations 2.7 and 2.8 , for the thermocouples in

series, we obtain:

E e wi

i

ii= =∑ ∑ ε

δ (2.9)

Because all the thermocouples are identical, Equation 2.9 can be expressed as follows:

E w E Wi= =∑ε

δεδ

or

(2.10)

Equation 2.10 shows that the power dissipated in the vessel is directly correlated with the heat fl ux. The term ε / Δ corresponds to the calibra-tion factor of the calorimeter.

W

wi

ei g i

Δq i

Figure 2.7. Joule effect calibration principle.


Description of Different Heat Flux Calorimeters Used for Food Characterization

According to the Calvet principle, many different calorimeters have been designed with various temperature ranges, small and large volumes, with a broad range of sensitivity. In this chapter, we describe two different Calvet calorimeters ( www.setaram.com ) that are used worldwide in many food research laboratories.

High Sensitivity Heat Flux Calorimeter

The development of the very high sensitivity heat fl ux calorimeter ( www.setaram.com ) was mainly motivated by the limitations of the standard DSCs: small amount of sample, limited sensitivity, no possibility of interaction or mixing. It was designed to be used as a multipurpose calorimeter working in isothermal and scanning modes with batch and fl ow capacities on a signifi cant volume of sample (1 cm 3 ).

The calorimetric chamber is made of a highly thermal conductive block with two cylindrical cavities for the experimental vessels (sample and reference). The detectors are built with semiconducting Peltier elements, characterized for their high sensitivity compared with a standard thermocouple - based detector. For the temperature control of the calorimeter, two principles are used:

• A thermostatic loop of liquid fl ows around the calorimetric block for a temperature range from − 20 ° C to 120 ° C

• Different shields with Peltier elements are located around the calo-rimetric block to extend the use at a lower temperature for a tem-perature range from − 45 ° C to 120 ° C.

In both cases, the vessels are easily removed from the calorimetry block. This is a key point for the cleaning of the vessels when different types of foods, such as fatty compounds, gels, and proteins, are used. The tops of the calorimeters are opened to allow the introduction of fl uids (gas, liquid) by means of adapted and dedicated vessels. The thermostatic loop of liquid provides a prestabilizing ring at the upper part of the calorimeter that allows the liquid to preheat before entering the calorimetric chamber. According to the type of experiments to be


performed on food components, there are different experimental vessels for the batch or the fl ow applications (Figure 2.8 ).

The standard batch vessel is mainly used to investigate food com-ponents in a liquid or solid form in a closed system.

The batch - mixing vessel is composed of two chambers that allow isolation of each material before mixing in the calorimeter. The mixing operation is achieved by pushing the rod from outside. The batch high - pressure vessel is mainly dedicated to investigation of food components under pressure, especially for modifi cation of struc-ture (glass transition, polymorphism) when high pressure is applied. For such experiments, the calorimetric vessel is fi tted with a high - pressure gas panel (maximum pressure: 1000 bar) (Le Parlou ë r et al. 2004 ).

The fl uid - mixing vessel is designed to introduce a gas or a liquid into the vessel to interact with the sample inside. Before introducing a liquid, the liquid temperature is stabilized at the temperature of the calorimeter. The fl uid - mixing vessel makes possible the mixing of two liquids in situ in the calorimetric vessel using an adapted mixer. The entering liquids are prestabilized at the temperature of the calorimeter and are introduced through micropumps at variable fl ow rates. Table 2.2 gives an overview of the variety of applications that can be per-formed with the different vessels.

Figure 2.8. Standard and mixing vessels (batch), fl uid circulation vessel (fl ow).

Table 2.2. Applications of the MicroDSC technique versus the vessel and the heating mode.

Vessel Heating Mode Component Application

Batch Scanning Protein (animal, cereal … )

Denaturation, aggregation, lyophilization

Isothermal Protein Crystallization Scanning Enzyme denaturation, stability Scanning Starch Gelatinization,

retrogradation, glass transition

Isothermal Starch Crystallization (stalling) Scanning Milk Melting, crystallization,

denaturation, aggregation Scanning Fat Melting, crystallization,

lipidic transition, polymorphism

Isothermal Fat Crystallization Scanning Hydrocolloids Melting, gelation Scanning Sugar Melting, crystallization,

glass transition (amorphism)

Isothermal Aroma Stability Batch high

pressure Scanning Fat, chocolate Polymorphism versus

pressure Starch Glass transition versus

pressure Batch

mixing Isothermal Enzyme Enzymatic reaction

Starch Wetting Dairy bacteria Yogurt processing Yeast Dough and bread processing Bacteria Bacteria growth, food safety

One fl uid vessel

Isothermal Oil Oxidative stability

Two - fl uid mixing vessel

Isothermal Enzyme Enzymatic reaction

28


The Mixing and Reaction Heat Flux Microcalorimeter

The mixing and reaction microcalorimeter ( www.setaram.com ) is used for larger amounts of materials to better fi t with the experimental needs of the food industry. The microcalorimeter can be used as a DSC for temperature scanning, but with large - volume samples. It is, however, more suitable for the applications in the isothermal mode. The micro-calorimeter has a large experimental volume (15 cm 3 ). It is built around a metallic conductive block with two cavities that contain the thermo-piles, which are made of crowns of thermocouples. The block itself is surrounded by the heating element and arranged in an insulated chamber. The calorimeter can be fi tted on a rotating mechanism to use with a special mixing vessel.

The microcalorimeter offers a large choice of experimental vessels for use with various applications. The most commonly used vessels in food research are as follows:

• The batch standard vessel is designed for investigating transforma-tion during heating or cooling a large volume of samples in the solid or liquid form. It also can be used to determine heat capacity.

• The batch high - pressure vessel is designed for simulation of reaction and decomposition under pressure in a closed vessel or under con-trolled pressure (max: 100 bar). It is used to defi ne safety conditions of some food - processing operations and also for simulation of super-critical gas extraction.

The gas - fl ow vessel is fi tted with two coaxial tubes and is used to produce a circulation of gas (inert or active) around the sample. It is used for investigation of oxidative stability of foods.

The mixing vessel using the rotating mechanism is divided into two chambers and separated by a metallic lid. One of the materials is placed in the lower chamber (i.e., powder) and the other material is placed in the upper chamber (i.e., liquid). The mixing of the two components is provided by rotating the calorimeter, the metallic lid acting as a stirrer. This mixing vessel is designed for investigation of liquid - liquid mixing (dilution, neutralization) or solid - liquid mixing (dissolution, hydration, wetting).

The membrane mixing vessel is used for mixing of viscous samples, often seen with food components and for applications in which the rotation of the calorimeter cannot be used. In such a vessel, the separation between both chambers is achieved with a thin membrane


(metal or PTFE). The vessel is fi tted with a metallic rod that is operated from outside the calorimeter. The mixing of components is obtained by pushing the rod to break the membrane. The rod is also used as a stirrer during the test.

The ampoule mixing vessel is designed for a slow dissolution process and for a wetting operation. The sample is sealed under vacuum in a breakable ampoule. The vacuum operation allows desorbing the surface of the solid sample for easier dissolution. The sealed ampoule and the solution are introduced into the vessel. By breaking the ampoule, the solid and liquid samples are brought into contact.

The Table 2.3 gives an overview of the major calorimetric applica-tions, either in scanning or isothermal modes.

Methods of Microcalorimetry in Food

Microcalorimetry offers a variety of methods that are applied to the characterization of foods and their components.

Heat Capacity Determination

Heat capacity plays an important role in thermal process and in refrig-eration applications. Heat loads, processing times, and industrial equip-ment sizes are infl uenced by the heat capacity of the material. Combined with thermal conductivity and thermal diffusivity, heat capacity data are needed for modeling of the thermal processes. Heat capacity varies with temperature and composition, as well as water content (Kaletun ç , 2007 ). Because food material can be in solid or liquid form, different ways of measuring heat capacity using the calorimetric techniques have been developed.

Heat capacity is thermodynamically defi ned as the ratio of a small amount of heat Δ Q added to the substance to the corresponding small increase in its temperature dT :

C Q

dT= δ

(2.11)

For processes at constant pressure, the heat capacity is expressed as:

C H

Tpp

= ⎛⎝

⎞⎠

δδ

(2.12)


Table 2.3. Applications of the C80 calorimetric technique versus the vessel and the heating mode.

Vessel Mode Component Application

Batch standard Scanning Starch Gelatinization, retrogradation Salt Solubility Carbohydrate Melting, crystallization,

amorphism, decomposition Batch high

pressure Scanning Coffee Safety (roasting),

supercritical CO 2 extraction

Cereal Self - ignition, explosion (powder)

Starch Gelatinization under pressure, glass transition versus pressure

Fat, chocolate Polymorphism versus pressure

Gas fl ow Scanning, isothermal

Oil Oxidative stability

Mixing (reversing)

Isothermal Oil Neutralization Sugar Dissolution Salt Dissolution Enzyme Enzymatic reaction Hydrocolloid Binding

Mixing (membrane)

Isothermal Starch Wetting, gelatinization Yeast Fermentation

Mixing (ampoule)

Isothermal Food powder Wetting, dissolution

Although DSC is a technique well suited to measure heat capacity (Richardson and Charsley 1998 ), essentially only one procedure has been developed using a continuous heating mode for solid samples. In this chapter, another procedure is described using a step - heating mode.

Heat c apacity d etermination in t emperature - s canning m ode If there is no conformational or phase transformation for the tempera-ture range considered, the calorimetric signal for a given mass of sample heated at a constant heating rate dT / dt is relative to the follow-ing relation for the sample side:


dqdt

m c m c dTdts

s p s cs p cs⎛⎝

⎞⎠ = +( )( ) ( )

(2.13)

where m s and m cs are, respectively, sample mass and vessel mass (including the cover) and c p ( s ) and c p ( cs ) are, respectively, specifi c heat capacity of the sample and its vessel.

For the reference side, an empty vessel is used giving the corre-sponding signal:

dqdt

m c dTdtr

cr p cr⎛⎝

⎞⎠ = ( )( )

(2.14)

where m cr is reference vessel mass and c p ( cr ) is specifi c heat capacity of reference vessel (equal to c p ( cs ) ).

The resulting differential calorimetric signal dq / dt is given by the following equation:

dqdt

m c m c m c dTdts p s cs p cs cr p cr

⎛⎝

⎞⎠ = + −( )( ) ( ) ( )

(2.15)

To get rid of the thermal effect generated by both vessels, the same test (called blank test) is run with identical empty containers. The fol-lowing equation describes the blank test heat fl ow.

dqdt

m c m c dTdtb

cs p cs cr p cr⎛⎝

⎞⎠ = −( )( ) ( )

(2.16)

By subtracting the two calorimetric traces, the specifi c heat capacity of the sample is extracted (Figure 2.9 ).

c

mdqdt

dqdt

dTdtp s

s b( ) = ⎛

⎝⎞⎠ − ⎛

⎝⎞⎠

⎡⎣⎢

⎤⎦⎥

1

(2.17)

As described in the calibration section, the Joule effect technique allows conversion of calorimetric signal in milliwatts without the need of standard reference materials. Therefore, in Equation 2.17 , all of the parameters (sample mass, calorimetric signals, heating rate) are accu-


rately known to determine the specifi c heat capacity of the sample c p ( s ) (expressed in J.g − 1 . ° C − 1 ) at a given temperature. For DSC technique, a third test is needed using a standard reference material (sapphire) that has a known specifi c heat capacity.

c p d etermination in the t emperature s tep m ode The technique described in previous section is easy to use, but has a drawback regarding the accuracy of the c p determination. Using the temperature scanning mode, the sample is continuously heated and is never at the thermal equilibrium. However, c p is a thermodynamical parameter, defi ned at the thermal equilibrium. The temperature step mode has been developed to address this limitation. A temperature step is applied to the sample, and the thermal equilibrium is established (characterized by return of the baseline) after each step. If Equation 2.15 is integrated from time t 0 (beginning of the step) to time t n (return to the baseline), the corresponding equation is obtained:

Q m c m c m c Ttt

s p s cs p cs cr p crn[ ] = + −( )( ) ( ) ( )0

Δ (2.18)

where c p corresponds to the mean c p value between the two tempera-tures defi ning the step of temperature. Q is obtained by integrating the corresponding surface defi ned by the calorimetric signal between t 0 and t n . The signal corresponding to the blank test is subtracted when

time

Heat flow (mW)

Ab

As

T

Figure 2.9. c p determination in the temperature - scanning mode.


an identical step of temperature is applied to obtain the fi nal equation for the mean c p of the sample.

c

mQ Q Tp s

sb( ) = −( )1 Δ

(2.19)

In Equation 2.19 , the result is independent of fl uctuations of the baseline between the tests contrary to that of specifi c heat determina-tion in temperature scanning mode.

c p d etermination for l iquids Both methods described above apply mainly for the c p determination of solid and powder foods. They also can be used for liquids, but the c p contribution of the vapor above the liquid sample must be accounted for to have an accurate measurement. The correction can be obtained by using a vessel designed for the c p determination of liquids (Cerdeirina et al. 2000 ). The vessel is a cylindrical container with a tube welded on the top (Figure 2.10 ). The liquid is introduced in the calorimetric vessel via the tube using a syringe with a long needle, which allows a complete fi lling of the vessel without a vapor phase. As the tube is opened, the liquid will freely expand when heating. The

Figure 2.10. Liquid c p vessel and principle.


c p determination is run for a given volume V of liquid, located in the calorimetric detection zone. If Q 0 is the differential calorimetric area corresponding to an increase Δ T of the temperature of the calorimeter when the two vessels (sample and reference) are empty, Q 1 when the measure vessel is fi lled with a standard liquid of known heat capacity, and Q 2 with the liquid to be investigated, the following equations are obtained:

Q Q V T S cp1 0 1 1− = ⋅ ⋅ ⋅ ⋅Δ ρ (2.20)

Q Q V T S cp2 0 2 2− = ⋅ ⋅ ⋅ ⋅Δ ρ (2.21)

where S is calibration coeffi cient of the calorimeter, V is volume of the vessel, ρ 1 and ρ 2 are masses of standard and sample, and c p 1 and c p 2 are heat capacities of standard and sample. The heat capacity of the liquid sample, at a given temperature, is obtained without needing to know and measure the corresponding volume V :

c Q Q

Q Qcp p2

1 0

2 0

1

21= −( )

−( )⎡⎣⎢

⎤⎦⎥

ρρ

(2.22)

The determination of the specifi c heat capacity requires the measure-ment of the density of the liquid sample. This c p measurement does not need vapor phase correction.

Heat c apacity of f oods The specifi c heat of foods depends on their composition, specifi cally the water content (Kaletun ç 2007 ). Table 2.4 gives an overview of the specifi c heat of selected foods above and below freezing ( www.engineeringtoolbox.com ).

Heating Mode

Microcalorimetry is used under the various heating modes are described next.

Scanning c alorimetry The scanning mode (heating or cooling) is the usual method that applies to the standard DSC technique. A microcalorimeter also can


be used as a DSC, but with low or very low scanning rates (less than 2 ° C.min − 1 ). Longer time of experimentation may be considered to be a disadvantage, but it provides a better resolution of different thermal processes.

Melting and c rystallization Physical state transformations (crystal-lization, melting, polymorphism) in fat samples are associated with thermal effects that are easily measured by DSC. Microcalorimetry used in the scanning mode allows improvement of the resolution of different effects because of low scanning rate, especially for charac-terization of emulsions (Relkin and Sourdet 2005 ).

Denaturation and a ggregation Proteins are the food components most studied by the microcalorimetric technique and include studies of conformation changes of food proteins (animal, vegetable, plant), food enzymes and enzyme preparations for the food industry, as well as effects of various additives on their thermal properties.

The denaturation and aggregation processes in thermal gelation of whey proteins were resolved with the microcalorimetric technique (Fitzsimons et al. 2007 ). Numerous previous studies of the thermal gelation of whey proteins, carried out on conventional (fast - scanning)

Table 2.4. Heat capacity data for some foodstuffs.

Food Category Type Cp before Freezing (J g − 1 ° C − 11 )

Cp above Freezing (J g − 11 ° C − 11 )

Fruit Apple 1.76 3.64 Grapefruit 1.84 3.81 Orange juice 1.8 3.73

Vegetable Cabbage 1.88 3.94 Potato 1.72 3.43

Meat Pork (bacon) 1.05 1.51 Pork (ham) 1.42 2.6

Fish Salmon 1.55 2.97 Carp 1.72 3.43

Dairy Butter 1 1.26 Cream 1.88 3.77 Milk (cow) 1.97 3.77 Milk (coconut) 1.76 3.98 Ice cream 1.67 3.1


DSC calorimeters (typical sample mass 15 – 50 mg), have shown only endothermic transitions. Slow transfer of heat into a large vessel (850 mg of sample) allows the exothermic heat fl ow from the slow aggregation process to keep pace with the endothermic heat fl ow from the more rapid denaturation process and give a detectable exotherm (Figure 2.11 ). The same resolution effect with a slow scanning rate has also been noticed on pea storage protein, vicilin (Bacon et al. 1989 ), on bovine serum albumin (Barone et al. 1992 , 1995 ), and on ovalbumin (Hagolle et al. 1997 ; Relkin 2004 ).

Gelation Microcalorimetry is applied to investigation of gels formed by biopolymers, such as carrageenan (Williams et al. 1991a , 1992 ), xanthan (Williams et al. 1991a , b ), gellan (Miyoshi et al. 1995 ; Robinson et al. 1991 ), agar (Cooke et al. 1996 ), pectin, and gelatin. Polysaccharides are widely used for their gelling and thickening properties in the food industry. In presence of a cation (for example, potassium K + ), a solu-tion of kappa - carrageenan gives an aggregate structure during heating. The temperature of transformation and the reversibility of the reaction

3.2

100

9085

83.1

3.0

2.9

Heat flow

(m

W)

2.8

2.7

2.6

2.5

2.440 50 60 70

Temperature (°C)

80 90 100

Figure 2.11. DSC heating scans (1.0 ° C/min) of 3.0 wt.% WPI pH 7.0) in the presence of NaCl at the different concentrations (80, 85, 90, and 100 mM NaCl). From Fitzsimons et al. (2007) .


(melting/gelation) can be obtained from the calorimetry data. Furthermore, detection of the transition depends not only on the poly-saccharide concentration but also on the product type. For xanthan and gellan, the energy associated with the transition is very weak, and the high sensitivity of the microcalorimeter is needed.

Gelatinization and r etrogradation Microcalorimetry is used to charac-terize the gelatinization behavior of starches and interaction of starch with other food components, as well as phase transitions during baking processes (Eliasson 2003 ). Calorimetry in the scanning mode is used not only to study the order - disorder behavior of starch during gelatiniza-tion but also to study the recrystallization (retrogradation) during storage (Berland et al. 2003 ). Crystallization can also be investigated in the isothermal mode. A special calorimetric vessel has been designed to investigate the starch gelatinization during cooking of pasta (Riva et al. 1991 ).

Isothermal c alorimetry Isothermal calorimetry is commonly used to simulate a process that occurs at a constant temperature or to check the storage stability of a food component (Sch ä ffer and Lorinczy 2005 ). When reactions and transitions take place within a food system, the kinetic parameters of reactions and transitions are obtained from analysis of isothermal calo-rimetric curves (Riva and Schiraldi 1993 ).

Shelf l ife Shelf life of foods (Franzetti et al. 1995 ; Riva et al. 1997, 1998, 2001 ) is investigated using isothermal calorimetry by continu-ously monitoring the kinetics of microbial growth or enzymatic activ-ity in fresh foods, such as whole eggs, fresh milk, and fresh carrots (Figure 2.12 ), or growth of bacteria in milk (Berridge et al. 1974 ). There are studies in the literature reporting the evaluation of bacterio-logical quality of seafood (Gram 1992 ), characterization of the thermal consequences of irradiation of bacteria (Moh á csi - Farkas et al. 1994 ), and microbial degradation (Teeling and Cypionka 1997 ; Andlid et al. 1999 ) by using isothermal calorimetry.

Oxidative s tability Thermal oxidative decomposition of edible oils examined by calorimetry can be used for predicting oil stability under normal or high pressure of oxygen.


Isothermal c rystallization The investigation of crystallization in the isothermal mode requires a high stability of the baseline of the micro-calorimeter combined with a high sensitivity because such a test may last many hours. This type of experimental protocol is applied to iso-thermal crystallization of proteins, isothermal crystallization of fats, or isothermal retrogradation of starch.

0.14

0.12

0.1

T=21.7°C

T=24.6°C

T=19.6°C

T=24.6°C

T=19.6°C

T=14.6°C

T=14.7°C

T=14.7°C

T=9.6°C

A-PASTEURIZEDWHOLE EGG

B-PASTEURIZED

WHOLE MILK

C-FRESH CARROTS

0.08

0.06

0.04

0.02

00 2 4 6

Time / days

Time / days

Time / days

8

0 21 43 5 6 7 8

0 10.5 1.5 2 2.5 3

10

0.14

0.12

0.1

0.08

0.06

0.04

0.02

0

0.12

0.1

0.08

0.06

0.04

0.02

0

exo

HF

/ m

W g

–1

HF

/ m

W g

–1

HF

/ m

W g

–1

exo

exo

Figure 2.12. Isothermal traces at different temperatures for pasteurized whole egg, pasteurized whole milk, and fresh carrots. From Franzetti et al. (1995) .


Step h eating in c alorimetry Step heating (or cooling) calorimetry is a technique that is between the two previously described modes. A small variation of temperature is applied to the sample by step. After each step, the sample is maintained at a constant temperature for a certain period of time. The relevance of the stepwise methods resides primarily in the ability to follow step by step the differential structural changes as a function of the tempera-ture. The technique was applied to follow the kinetics of the gelation of gelatine (Cuppo et al. 2001 ) (Figure 2.13 ).

Mixing and Reaction Calorimetry

For investigation of mixing and reaction processes in foods, the micro-calorimetry has major advantages over the DSC. As described previ-ously, the larger capacity of the calorimetric chamber allows the design of specifi c mixing vessels. Liquid - liquid or solid - liquid interactions are evaluated by mixing obtained by stirring or ampoule breaking.

Mixing can be performed by two different modes:

1. Batch mixing: The two components A and B are brought into contact in the mixing vessel. The heat of mixing corresponds to a given concentration of A or B.

0.100 5 10 15 20 25 30 35 40

0 5 10 15 20

time / hours

T / °C

25 30 35 40

0.08

0.06

0.04

0.02

0.00

40

30

20

10

0

heat flow

/ m

W g

–1

Figure 2.13. Stepwise heating thermogram (from 0 to 40 ° C) of 4.5% gelatine (LH1e) in aqueous 0.1 M NaCl. From Cuppo et al. (2001) .


2. Flow mixing: The two components A and B at a given fl ow rate are pumped and mixed in the vessel. The concentration of the mixture can be adjusted by modifying the fl ow rate of A or B.

Dissolution, s olubility In food industry, solid - liquid and liquid - liquid interactions are often encountered, such as dissolution of powder (sugar, salt) and solubility of proteins, lipids, and fi bers. For such studies, the batch - mixing vessel is ideal because it provides information relevant to the start of the reaction and the corresponding kinetics.

Neutralization The batch - mixing vessel is also convenient for the investigation of any reaction occurring during a food process, such as neutralization of edible oils by soda. Raw edible oils contain free fatty acids that have to be neutralized before being used. The amount of soda necessary for neutralization has to be adjusted based on the acidity of the oil. The simulation of the operation was performed on a microcalorimeter using edible oil with variable acidities (Figure 2.14 ).

Binding A mixing calorimeter is useful to investigate the impact of the weak nonspecifi c physical interactions of the food biopolymers (proteins,

heat flow

4 mW

rape seed 8,8 %

peanut 3 %

peanut 0,85 %

exo

0 5 10 15 20 25

time (mn)

30

1

2

3

1

2

3

Figure 2.14. Neutralization of free acidity in edible oil by soda (C80).


polysaccharides) with each other and with the major low - molecular - weight ingredients of the multicomponent food colloids (sugars, mineral salts, small - molecule surfactants). The structure formation in the bulk aqueous phase and at the interfaces of colloidal systems, as well as the functional properties, depend on these weak interactions (Semenova 2007 ).

Enzymatic r eactions The example for the enzymatic reaction of transformation of maltose using glucoamylase illustrates the two modes of mixing. In batch mode, 30 mg of maltose powder is mixed with 20 μ l of glucoamylase. The corresponding exothermic effect indicates the transformation of maltose for a given concentration of enzyme (Figure 2.15 ). In fl ow mode, maltose (1% in H 2 O) is circulated in both tubes of the mixing vessel to establish the baseline of the test. Then, glucoamylase (1% in H 2 O) is introduced. A corresponding exothermic deviation measures the effi ciency of the enzyme for maltose transformation at a given temperature and concentration. When the enzyme fl ow is replaced by maltose, the calorimetric signal is returned to the baseline (Figure 2.16 ). This mixing mode is fl exible because various enzyme concentra-tions can be tested consecutively.

HEAT FLOW

0.5 1.0 1.5 2.0 TIME (h)

50μW

2.5

Figure 2.15. Enzymatic reaction (maltose + glucoamylase) at 33 ° C in batch mode (MicroDSCIII).


Fermentation, b acterial g rowth Isothermal microcalorimetry provides informative data regarding microbial growth and microbial metabolism involving yeast and bac-teria. Following are some examples of applications of isothermal microcalorimetry:

• Dosage of antibiotics or detection of antibiotics in milk • Control of alcoholic fermentation • Control of panifi cation • Control of production of dairy products (lactic acid bacteria) • Control of biomass reaction • Control of bacterial growth

Microcalorimetry was used to investigate the growth of probiotic cultures (Sch ä ffer, Szakaly, and Lorinczy 2004 ; Sch ä ffer and Lorinczy 2005 ). The process of yogurt production also was simulated by mixing yogurt containing lactic acid bacteria with milk at 37 ° C. The exother-mic effect is associated with the bacterial growth and the characteris-tics of the fi nal product, according to the temperature, the pH, and the quality of the milk.

Pressure Calorimetry

High - pressure processing is an important application for food research. It is important to understand the pressure - dependent phase change phe-

HEAT FLOW

0 10 20 30 Time (min)

maltose+

maltose

maltose+

maltose

enzyme+

maltose

20μW

Figure 2.16. Enzymatic reaction (maltose + glucoamylase) at 25 ° C in fl ow mode (0.3 ml.min − 1 , MicroDSCIII).


nomena in food to improve and develop new technologies. High hydro-static pressure can cause denaturation of proteins, solidifi cation of lipids, inactivation of microorganisms, and destabilization of biomembranes. The calorimetric technique is used to investigate the transformations induced by pressure. Because a commercial high - pressure calorimeter is not available (see Chapter 3 ) for high hydrostatic pressure applications, the sample is processed in a specifi c pressure device, outside the calo-rimeter. This application was used to investigate the protein denaturation in egg white after high - pressure processing (Andrassy et al. 2006 ) to detect the structural changes in milk protein beta - lactoglobulin induced by combined effects of pressure and temperature (Tedford and Schaschke 2000 ; Kolakowski et al. 2001 ). High - pressure induced modifi cations of soy protein in soy milk, studied using microcalorimetry, showed that denaturation of b - conglycinin and glycinin occurred at 300 MPa and 400 MPa, respectively, as judged by the absence of endothermic peaks in thermograms of pressure - treated samples (Zhanga et al. 2005 ).

Microcalorimetry in the scanning mode also was used to evaluate the relative high hydrostatic pressure resistance of bacterial strains from Staphylococcus aureus (Figure 2.17 ) and Escherichia coli in

20 40

Heat Flow0.5 mW

60 80

Temperature (°C)

100 120

B

A

Figure 2.17. Calorimetric traces of untreated control (A) and pressure - treated (345 MPa, 35 ° C, 10 min) (B) Staphylococcus aureus. From Alpas et al. (2003) .


vivo. The total apparent enthalpy change and thermal stability were two calorimetric parameters used to compare bacterial strains of untreated control and pressure - treated bacteria (Alpas et al. 2003 ).

Pressure can be applied on the sample inside the calorimetric vessel. A maximum gas pressure of 100 MPa was reported by Le Parlou ë r et al. (2004) to be used for investigation of polymorphic changes in fatty compounds, the modifi cation of the glass transition temperature for food with amorphous phase (sugar, starch, dough, frozen foods), or oxidative stability of oils. Supercritical extraction process using CO 2 as a fl uid was simulated in a high - pressure calorimeter with an adapted vessel (Stassi and Schiraldi 1994 ). Such a setup allows investigation either at constant pressure and at variable supercritical CO 2 fl ow rate, or as batch system with a variable pressure. The latter allows the moni-toring of the solubility - pressure relationship.

Calorimetry under Controlled Relative Humidity

Many food processes (extrusion, baking, drying, milling) may generate amorphous compounds. Their stability upon storage, especially in a humid atmosphere, has to be controlled. Water induces plasticization and leads to depression of the glass transition temperature, causing signifi -cant changes in the physicochemical and crystallization properties of the food components containing an amorphous phase. Scanning calorimetry is recognized as an effi cient technique for measurement of the glass tran-sition temperature of hydrated products (Bizot et al. 1997 ; Borde et al. 2002 ). Scanning calorimetry was also used to monitor crystallization of lactose in humidifi ed powders, because lactose crystallisation and Maillard reaction are two major modifi cations occurring in milk and whey powders during processing and storage (Morgan et al. 2005 ). Typically, samples are equilibrated outside the calorimeter, and the infl u-ence of water content or water activity are investigated using calorimetry. However, the sample can be prepared with a defi ned water content, and thermal properties can be measured inside a special vessel by combining DSC and humidity generator (Le Parlou ë r and Mathonat 2003 ).

Conclusion

As in other industrial domains, the food industry is more and more interested in applying new techniques of investigations to improve the


products, to enhance quality and safety, to fi nd new ingredients, and to better understand the different processes involved in the food pro-duction. Although microcalorimetry is not a new technique, the broad range of applications for calorimetry is not well known in the area of food technology. As described in this chapter, the possibilities of using different types of calorimeters are unlimited and can be applied to a large variety of foods. Because heat is involved in most food transfor-mations during processing and storage, calorimetry will provide answers to food technologists in their daily research.

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Chapter 3

High - Pressure Differential Scanning Calorimetry

G ü nther W.H. H ö hne and G ö n ü l Kaletun ç

51

Introduction 51 Construction of the High - Pressure DSC 53 Calibration of the High - Pressure DSC 57

Temperature Calibration Procedure 58 Heat Calibration Procedure 61

Applications of the High - Pressure DSC 63 Conclusion 63 References 64

Introduction

Pressure is an essential variable in physical chemistry. Measurements at different pressures are therefore of great importance from the ther-modynamic perspective. The change of pressure provides increased insight into the thermodynamic behavior of materials. The wider the pressure region, the better description of material response to pressure is obtained, which enables one to develop predictive capability. Higher pressure is often used during production and processing of materials, and the change of properties that occurs with pressure is therefore of great interest for optimization of processing conditions. In particular, the latent heat of reactions, phase and conformational transitions, and their pressure dependence are valuable information for quantitative


analysis of systems under study both in basic research and in industrial processing. There is an increasing interest in application of high pres-sure for food processing and preservation. Although the main goal of this application is to produce microbiologically safe food, high pres-sure affects the high - molecular - weight components of a food product, causing conformational and phase changes. Proteins and starches con-stitute a large percentage of many food products. The phase or confor-mational changes in such compounds may cause appearance or textural changes that might affect the quality of the fi nal food products; there-fore, the effects of high - pressure processing on food products and their individual components need to be determined.

Calorimetry is well - suited for study of food materials because food processing involves either heating or cooling of materials, which can be simulated in a calorimeter so that data can be directly related to the process protocols (Miles, Mackey, and Parsons 1986 ; Mackey et al. 1991 ; Lee and Kaletun ç 2002a, 2000b ). If the food products are pro-cessed by other means, such as using high pressure or chemicals, temperature - scanning calorimetry may used to compare thermograms of food product before and after exposure to treatment to evaluate its effect (Niven, Miles, and Mackey 1999 ; Alpas et al. 2003 ; Kaletun ç et al. 2004 ; Lee and Kaletun ç 2005 ). However, this approach provides information about only irreversible changes that occur in the food product. Therefore, there is great interest in high - pressure calorimetry to characterize the changes in a sample and to determine the thermal properties, such as heat capacity, under the conditions relevant to high - pressure processing.

Some commercially available calorimeters that operate at rather moderate pressures of up to 100 MPa (1000 bar) exist. Unfortunately, there is no truly high - pressure calorimeter on the market (i.e., working at pressures well above 100 MPa). One major limitation is the need for a fl uid medium (gas or liquid) to transfer the pressure to the sample, and any liquid or highly compressed gas has a rather large thermal conductivity (compared with a gas at ambient pressure). The heat fl ows through the pressure medium rather than through the thermal pathway constructed inside the calorimeter to measure the heat fl ow. Consequently, a large thermal leakage occurs, leading to much reduced calorimeter sensitivity.

These problems and the fact that high pressures up to 500 MPa (5 kbar) or even more are not easy to handle due to the need for special

High-Pressure Differential Scanning Calorimetry 53

equipment and their potential danger have impeded the development of a commercially available high - pressure calorimeter. Currently, researchers in the fi eld construct their own equipment (see Chapter 13 ). Although high - pressure differential thermal analysis (DTA), a non-quantitative caloric method, exists in several laboratories worldwide at pressures up to 1 GPa (e.g., see Szab ó et al. 1969 ; Shulgin and Godovsky 1992 ; Schmidt et al. 1994 ; Nakafuku and Sugiuchi 1996 ), the number of high - pressure calorimeters remains small. The most widely used calorimeter type is the differential scanning calorimeter (DSC; see Chapter 1 ), and some high - pressure differential scanning calorimeters (HP - DSC) have been constructed during the last three decades. Different research groups have approached the several prob-lems of HP - DSC in different ways (Schmidt et al. 1994 ; Arntz 1980 ; Kamphausen 1975 ; Sandrock 1982a ; Eichler and Gey 1979 ; Mellander, Baranowski, and Lund é n 1981 ; Randzio 1983 ; Schneider 1985 ; Zhu et al. 2004a ). To our knowledge, only one power - compensated DSC (based on the PerkinElmer DSC - 7, PerkinElmer, Waltham, Massachusetts) exists, and it works up to a pressure of 500 MPa (5 kbar) (Blankenhorn and H ö hne 1991 ). H ö hne and co - workers (Blankenhorn and H ö hne 1991 ; Ledru et al. 2006 ) modifi ed a commercial power - compensated DSC (PerkinElmer DSC 7) by building a new high - pressure measuring head, rather than building a completely new high - pressure DSC. In this chapter, we focus on the construction of this calorimeter because it was demonstrated that it can generate calo-rimetric data at high pressures successfully (H ö hne and Blankenhorn 1994 ; H ö hne, Schawe, and Shulgin 1997 ; H ö hne 1998 ; Rastogi, H ö hne, and Keller 1999 ; H ö hne, Rastogi, and Wunderlich 2000 ; Ingram et al. 2008 ), and it is possible to build similar ones without signifi cant dif-fi culty (Ledru et al. 2006 ).

Construction of the High - Pressure DSC

The HP - DSC presented here operates on the power compensation principle, making use of a commercial DSC (PerkinElmer). The origi-nal measuring head was replaced with a head built by H ö hne and co - workers (1991) with the same electrical and sensor properties but positioned inside an autoclave that can be pressurized by means of a hand - operated spindle pump (Figure 3.1 ).


High - pressure handling is a dangerous task. To illustrate, note that a pressure of 500 MPa (5 kbar) is nearly two times the pressure inside a gun being shot. If a part of the autoclave fails during an experiment under high pressure, the effect may potentially be worse than that from a bullet. Consequently, there are high safety demands on high - pressure experiments. Usually, an autoclave driven with gas as the pressure medium must be operated in a separate high - pressure shelter room, and the operator must be outside in a safe place. To avoid such trouble, we gave preference to silicone oil as the pressurizing medium. Using a liquid pressure medium (usually oil) for high - pressure experiments is still rather dangerous, and proper steps to avoid accidents have to be taken, but the measures are by far less expensive than that used with highly compressed gas.

Because of these safety reasons, the complete high - pressure system, including the autoclave (Figure 3.2 ), the spindle pump, high - pressure lines, valves, and transducers, were provided by a high - pressure spe-cialist (SITEC - Sieber Engineering AG, Switzerland). The HP - DSC head consists of two silver furnaces constructed by the research group and is located within ceramic housings (Figure 3.3 ). To avoid greater heat loss, the size was chosen in order to fi t closely within the autoclave but without direct solid - to - solid contact. The furnaces (Figure 3.4 )

Figure 3.1. High - pressure DSC setup. (a) DSC; (b) spindle pump; (c) autoclave (Ledru et al. 2006 in accordance with Blankenhorn and H ö hne 1991 ) .

Figure 3.2. High - pressure DSC: Autoclave unit (Ledru 2006 in accordance with Blankenhorn and H ö hne 1991 ) .

Figure 3.3. High - pressure DSC: Ceramic housing with the silver furnace inside (Ledru et al. 2006 in accordance with Blankenhorn and H ö hne 1991 ) .

55


were electrically isolated with special ceramic glue. They were pro-vided with platinum wire windings for both the heaters and sensors to match the resistance of the original DSC cell and with leads through the autoclave closing caps to the calorimeter control system.

The purpose of locating the two furnaces within the ceramic hous-ings is to isolate them, preventing cross talk between the sample and reference and minimizing the disturbance of the heat fl ow signal caused by convection currents in the oil. These housings contain holes for oil entry and escape during the fi lling/emptying and pressure change process, and they have screw caps (Figure 3.3 ) to allow their volume to be adjusted in order to balance the two furnaces to obtain a fl at baseline.

Using a branched silicone oil of approximately 100 mPa/s (Wacker AS 100, Wacker - Chemie GmbH, Germany), the HP - DSC may be operated in a temperature range from 20 ° C to 300 ° C at pressures from ambient to 500 MPa and with various heating and cooling rates (from 0.5 K.min − 1 to 20 K.min − 1 ). The actual pressure value within the auto-clave is measured using a pressure transducer close to the reference cell, which can be connected to a data logger for pressure recording.

The sample must, of course, be encapsulated to avoid any contact with the pressurizing medium. This is not an easy matter, as the encap-

Figure 3.4. High - pressure DSC: Silver furnace with sample pan inside (Ledru et al. 2006 in accordance with Blankenhorn and H ö hne 1991 ) .


sulation must be oil - tight on the one hand and free from air bubbles and empty space on the other. The latter would lead to large deforma-tion of the sample container when the pressure rises; thus, the container possibly would not remain oil - tight and the measurement would be faulty. There are several possible ways to solve the problem. One is to prepare the sample to fi t exactly between two aluminum crucibles that then are welded together with a proper press. Another possibility is to put the samples into crucibles of a plastic metal such as indium or lead and close the crucible hermetically.

In operation, the furnaces are placed within the autoclave with their axes horizontal. The autoclave has a lid (Figure 3.4 ) that screws down onto the crucible, ensuring that it is fi rmly located within the silver furnace and that good thermal contact is made.

The HP - DSC constructed this way had the following properties: pressure and temperature ranges from 0.1 to 500 MPa and from ambient to 600 K (330 ° C), respectively, and thermal noise 50 – 100 μ W peak to peak (much larger than in normal DSCs because of the oil convection). The detection limit for transitions (peak area) is about 5 mJ (i.e., 1 J g − 1 ). Compared to common DSCs, the baseline repeatability of the HP - DSC is poor (2 – 3 mW) because of unavoidable small differences in oil volume between the sample and reference cell when a new sample is remounted. This makes it impossible to determine heat capacities of a sample with the usual method, which is to subtract an empty pan run from the sample run. The change in baseline after remounting a new sample is often larger than the expected difference of the heat fl ow rate between the two runs. This unavoidable effect is a serious disadvantage of the high - pressure DSC.

Calibration of the High - Pressure DSC

To get precise and reliable thermodynamic data, a careful calibration of every calorimeter is necessary. For normal DSCs, this includes both temperature and heat calibration or heat fl ow rate calibration using standard procedures (H ö hne, Hemminger, and Flammersheim 2003 ) and certifi ed reference substances with well - known temperature and heat of transition values. Based on calibration procedure, a function or table of corrections is generated to obtain the true temperature and the true heat of transition from the measured quantities. As a rule, the


correction depends on temperature but may also be infl uenced by other parameters, such as sample mass, pan type, and heating rate (for details, see the textbook by H ö hne, Hemminger, and Flammersheim [ 2003 ]). Various reference substances with different transition temperatures are needed for a complete calibration. For the HP - DSC, however, the change of the calibration with the change of pressure must be taken into consideration, too. Sensitivity of every thermometer and every heat fl ow rate sensor is affected by pressure. This is particularly impor-tant for very high pressure levels used in the HP - DSC. Consequently, the resulting correction function for the HP - DSC always depends on at least two parameters, namely, temperature and pressure.

Unfortunately, there is no existing certifi ed data for the infl uence of pressure on the temperature and heat of transition for the recommended calibration reference substances. Among common reference substances such as indium, tin, lead, and zinc, only for indium is there reliable information in literature (H ö hne et al. 1996 ) regarding the pressure dependence of temperature and enthalpy of fusion. The respective literature for the high - pressure dependence of the melting point of tin (McDaniel, Babb, and Scott 1962 ; Sandrock 1982b ) and lead (McDaniel, Babb, and Scott 1962 ) differs a little. The pressure depen-dence of the melting enthalpy for tin and lead has, to our knowledge, not been reported; therefore, we assume that the melting enthalpy of tin and lead is almost pressure independent, similar to that of indium for the calibration of HP - DSC.

As a result, the calibration of the HP - DSC cannot be as precise as the calibration of normal DSCs. Consequently, the uncertainty of HP - DSC enthalpy measurements must be considered much higher than the uncertainty of common DSC measurements at ambient pressure.

Temperature Calibration Procedure

The measured temperature T meas has to be corrected ( T corr ) by adding a correction term based on temperature and pressure dependence:

T p T T p T T p Tcorr meas corr, , ,( ) = ( ) + ( )Δ (3.1)

Normally, the correction Δ T corr depends on the heating rate, too. This infl uence is, however, of minor importance if the same heating rate is always used for all measurements.


Calibration means the determination of the Δ T corr ( p , T ) function (for a certain heating rate) either as a best fi t function or, more often, in form of a table or array. The respective calibration procedure compares the measured temperature of transition at different temperatures and pressures with the true value of certifi ed reference substances. To check possible nonlinear dependences, at least three different transi-tions must be measured at more than three different pressures. Indium is the only reference substance for which the pressure dependence of the melting point is well known (H ö hne et al. 1996 ):

T p T pfusIn

fus,0InK K K MPa MPa( ) = [ ] + ±( )[ ]⋅[ ]−0 0507 0 003 1. . (3.2)

where Tfus,0In K= 429 7485. is the fi xed melting point of the ITS - 90 for

indium at normal pressure (Preston - Thomas 1990 ) and p is the pressure. The given standard deviation defi nes the limits of this best value estimation and leads to a maximum uncertainty of 1.5 K at 500 MPa.

For tin, to our knowledge two reliable publications exist for the pressure dependence of the melting point (McDaniel, Babb, and Scott 1962 ; Sandrock 1982b ). The best value approximation for these data reads:

T p T pfusSn

fus,0SnK K K MPa MPa( ) = [ ] + ±( )[ ]⋅[ ] −−0 0324 0 0025

1 45

1. .. ±±( )[ ]⋅[ ]− −4 86 10 6 2 2. K MPa MPap (3.3)

where Tfus,0Sn K= 505 078. , the fi xed melting point of the ITS - 90 for tin

at normal pressure (Preston - Thomas, 1990 ) and p the pressure. The standard deviations were defi ned from this best value estimation and, according to the literature data, lead to a maximum uncertainty of 2.5 K at 500 MPa.

For the pressure dependence of the melting point of lead, to our knowledge only one publication exists (McDaniel, Babb, and Scott 1962 ). Any best value estimation, therefore, cannot be performed for the pressure dependence of its melting point, and the uncertainty is not known. Consequently, lead cannot be used for calibration of the HP - DSC. Because we have two reference substances only, we have to restrict ourselves to a linear approximation of the temperature depen-dence of the correction function.


To calibrate the temperature of the HP - DSC the fi rst time at least three different In as well as Sn samples with a mass of 1 – 10 mg have to be weighted precisely ( ± 0.01 mg) and encapsulated as described above in hermetically sealed pans. With these samples, heating and cooling runs must be performed at different pressures and typical heating rates. To detect possible differences between the reference and the sample side of the HP - DSC, a small sample of the same calibrant should also be positioned on the reference side.

Δ T corr ( p , T ) is defi ned as the reference temperature minus the measured temperature at each pressure for both indium and tin, where the reference values are defi ned by Equations 3.2 and 3.3 for indium and tin, respectively. This then permits the construction of a calibration diagram in which the Δ T corr values are plotted against the measured temperature for both indium and tin for pressures from 0.1 MPa to 500 MPa, as shown in Figure 3.5 . In this fi gure, a linear relationship between the temperature correction and the measured temperature has been assumed, as is usual for two - point calibration, to enable an extrapolation to be made over a wider temperature range. For the temperature correction, a maximum uncertainty of 1.6 K at 500 MPa has been calculated from Equation 3.2 from the indium values. For

0.1 MPa

15

10

5

0

–5330 380 430

Measured temperature/K

ΔTcoor/K

480 530

50 MPa100 MPa150 MPa200 MPa250 MPa300 MPa350 MPa400 MPa450 MPa500 MPa

Figure 3.5. Example of temperature correction function for high - pressure DSC (according to Ledru et al. 2006 ) .


tin values, the maximum uncertainty of the correction is larger, namely, 3.1 K at 500 MPa.

This rather time - consuming procedure is absolutely necessary for a correct temperature calibration of the HP - DSC. The calibration should be verifi ed in regular periods. This can be done in a shortened proce-dure with only one sample and one heating rate at two or three pres-sures. If the result of the respective correction remains unchanged, no further action is needed. If not, the whole calibration procedure must be repeated. As noted, a small indium sample may be placed and left in the reference cell to verify the temperature calibration “ online, ” and then equation 3.1 , valid for the sample cell, may be rewritten for the reference cell as:

T p T p T p T Ttrue meas corr s-r( ) = ( ) + ( ) +Δ Δ, (3.4)

where Δ T corr ( p , T ) is the correction obtained from the calibration pro-cedure and Δ T s − r is an additional correction that corresponds to the difference between the reference and sample temperature sensor. This difference is often temperature and pressure independent and can be taken as a constant value compared with the overall uncertainty of the corrected temperatures, which are within the range of 1 – 4 K (depend-ing on temperature and pressure) for our calorimeter.

Heat Calibration Procedure

The measured heat or heat fl ow rate in a DSC is related to the true value as follows:

Δ Δfus true fus meas corrH p H p R p( ) = ( )⋅ ( ) (3.5)

where R corr ( p ) is the calibration factor. For power - compensated DSC, the calibration factor does not depend highly on temperature, but rather it is a function of pressure. The heat calibration is performed similarly to the usual procedure for common power - compensated DSCs. An indium sample is positioned in the HP - DSC, and the melting peak is measured at different pressures. The calibration factor k ( p ) is then determined by comparing the measured value with the reference value at the respective pressure:


R p H p

H pcorrfus ref

In

fus measIn( ) = ( )

( )ΔΔ

(3.6)

For the pressure dependence of the latter, the best value estimation from Preston - Thomas ( 1990 ) is used:

Δ Δfus refIn

fus refInJg Jg g MPa MPaH H p− − − − −= + ±( )[ ]⋅10

1 3 1 13 3 2 10, . [[ ] −±( )[ ]⋅[ ]− − −2 6 2 10 7 1 2 2. Jg MPa MPap

(3.7)

where Δfus refIn JgH0

128 62 0 11= ± −. . is taken as the best value for the heat of fusion of indium at normal pressure and p the pressure. This best value estimation includes an uncertainty of 0.1 J g − 1 at ambient pressure increasing to 1.1 J g − 1 at 500 MPa for the best value of the heat of fusion of indium. A best value estimation of the pressure depen-dence of the melting enthalpy for tin has not been reported; therefore, we did not use the melting enthalpy of tin for calibration purposes. The total uncertainty of a heat measurement with the HP - DSC is the sum of the uncertainties of the reference material and the respective mea-surement, which is about 0.9 – 2 J g − 1 . In Figure 3.6 R corr ( p ) resulting from such a heat calibration is given as an example.

Figure 3.6. Calibration factor in dependence on pressure for a high - pressure DSC (according to Ledru et al. 2006 ) .


Following the calibration of the HP - DSC, the measured values can be reliably corrected. The calibration must be repeated if any essential part of the high - pressure calorimeter is changed or replaced. This is especially necessary if the oil is changed, because the heat transfer con-ditions become different. It may be suffi cient to verify the calibration from time to time, at least when a different type of sample pan and scan-ning rate are used. To be on the safe side, it is recommended to always have a small indium sample in the otherwise empty reference pan to verify the calibration “ online ” during every measurement.

Applications of the High - Pressure DSC

To our knowledge, HP - DSC has rarely been applied to studying food samples to date (Zhu et al. 2004b ) because there are no HP - DSCs commercially available. The few working groups that have constructed such a device (Sandrock 1982a ; Blankenhorn and H ö hne 1991 ; Ledru et al. 2006 ) were mainly interested in polymer science (H ö hne 1999 ; Ledru et al. 2006 ) or organic chemistry (Sandrock 1982b ). Another problem arises from the demand to seal the samples hermetically in such a way that any cavity is avoided. For solid materials, this is rather easy, as described above. But it seems to be impossible to seal liquids or solutions, which often are matter of interest in food science, in this way. For liquid samples, special hermetically sealed pans must be developed and fi lled in such a way that no cavity or bubble remains inside after closure to avoid a huge deformation of the pan when the pressure rises.

Conclusion

High - pressure differential scanning calorimeters are not available commercially. They are, however, very valuable instruments for obtain-ing essential thermodynamic data, including food applications. If high - pressure measurements are needed, one must build such an instrument oneself. This is not an easy task and needs experience. As described above, the high - pressure power compensated DSC has been built and works well. The advantages of this type of high - pressure calori-meter are as follows:


• the well - tested construction, • well - established calibration procedures, and • compatibility with the widely used PerkinElmer DSCs and the well -

known power compensation method.

The disadvantages are the following:

• Sample size (and mass) is limited to 30 μ l. • The samples must be hermetically sealed. • The air - free sealing of liquid samples is very diffi cult.

Nevertheless, we believe that the high - pressure power compensated DSC can be very useful in food research applications. It should be possible to overcome its disadvantages, which will be easier than con-structing a new high - pressure calorimeter better suited for demands of food science or to modify other types of high - pressure calorimeters that currently exist.

References

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Arntz H. 1980 . New high pressure low temperature differential scanning calorimeter . Rev Sci Instrum , 51 ( 7 ): 965 – 967 .

Blankenhorn K. and H ö hne G.W.H. 1991 . Design, specifi cations and application of a high pressure DSC cell . Thermochim Acta , 187 : 219 – 224 .

Eichler A. and Gey W. 1979 . Method for the determination of the specifi c heat of metals at low temperatures under high pressures . Rev Sci Instrum , 50 ( 11 ): 1445 – 1452 .

H ö hne G.W.H. 1999 . High pressure differential scanning calorimetry on polymers . Thermochim Acta , 332 : 115 – 123 .

H ö hne G.W.H. and Blankenhorn K. 1994 . High pressure DSC investigations on n - alkanes, n - alkane mixtures and polyethylene . Thermochim Acta , 238 : 351 – 370 .

H ö hne G.W.H. , Dollhopf W. , Blankenhorn K. , and Mayr P.U. 1996 . On the pressure dependence of the heat of fusion and melting temperature of indium . Thermochim Acta , 273 : 17 – 24 .

H ö hne G.W.H. , Hemminger W. , and Flammersheim H.J. 2003 . Differential Scanning Calorimetry , 2nd revised and enlarged ed . Springer - Verlag : Berlin .


H ö hne G.W.H. , Rastogi S. , and Wunderlich B. 2000 . High pressure differential scan-ning calorimetry of poly(4 - methyl - pentene - 1) . Polymer , 41 : 8869 – 8878 .

H ö hne G.W.H. , Schawe J.E.K. , and Shulgin A.I. 1997 . The phase transition behaviour of linear polyethylenes at high pressure . Thermochim Acta , 296 : 1 – 10 .

Ingram M.D. , Imrie C.T. , Ledru J. , and Hutchinson J.M. 2008 . Unifi ed approach to ion transport and structural relaxation in amorphous polymers and glasses . J Phys Chem , 112 : 859 – 866 .

Kaletun ç G. , Lee J. , Alpas H. , and Bozoglu F. 2004 . Evaluation of structural changes induced by high hydrostatic pressure in Leuconostoc mesenteroides . Appl Environ Microbiol , 70 : 1116 – 1122 .

Kamphausen M. 1975 . New differential scanning high pressure microcalorimeter . Rev Sci Instrum , 46 ( 6 ): 668 – 669 .

Ledru J. , Imrie C.T. , Hutchinson J.M. , and H ö hne G.W.H. 2006 . High pressure differential scanning calorimetry: Aspects of calibration . Thermochim Acta , 446 : 66 – 72 .

Lee J. and Kaletun ç G. 2002a . Evaluation by differential scanning calorimetry of the heat inactivation of Escherichia coli and Lactobacillus plantarum . Appl Environ Microbiol , 68 : 5379 – 5386 .

Lee J. and Kaletun ç G. 2002b . Calorimetric determination of inactivation parameters of microorganisms . J Appl Microbiol , 93 : 178 – 189 .

Lee J. and Kaletun ç G. 2005 . Evaluation by differential scanning calorimetry of the effect of acid, ethanol, and NaCl on Escherichia coli . J Food Prot , 68 : 487 – 493 .

Mackey B.M. , Miles C.A. , Parsons S.E. , and Seymour D.A. 1991 . Thermal denatur-ation of whole cells and cell components of Escherichia coli examined by dif-ferential scanning calorimetry . J Gen Microbiol , 137 ( 10 ): 2361 – 2374 .

McDaniel M.L. , Babb Jr. S.E. , and Scott G.J. 1962 . Melting curves of fi ve metals under high pressure . J Chem Phys , 37 ( 4 ): 822 – 828 .

Mellander B.E. , Baranowski B. , Lund é A. 1981 . Transition enthalpies of silver iodide in the high - pressure region determined by DSC . Phys Rev , 23 ( 8 ): 3770 – 3773 .

Miles C.A. , Mackey B.M. , and Parsons S.E. 1986 . Differential scanning calorimetry of bacteria . J Gen Microbiol , 132 ( 4 ): 939 – 952 .

Nakafuku C. and Sugiuchi T. 1996 . Effect of pressure on the phase diagram of binary mixtures of n - alkanes . Polymer , 34 ( 23 ): 4945 – 4952 .

Niven G.W. , Miles C.A. , and Mackey B.M. 1999 . The effects of hydrostatic pressure on ribosome conformation in Escherichia coli : An in vivo study using differential scanning calorimetry . Microbiol , 145 : 419 – 425 .

Preston - Thomas H. 1990 . The international temperature scale of 1990 (ITS90) . Metrologia , 27 : 3 – 10 .

Randzio S.L. 1983 . A pressure - scanning calorimeter . J Physics E Sci Instrum , 16 : 691 – 694 .

Rastogi S. , H ö hne G.W.H. , and Keller A. 1999 . Unusual pressure - induced phase behavior in crystalline poly(4 - methylpentene - 1): Calorimetric and spectroscopic results and further implications . Macromolecules , 32 : 8897 – 8909 .


Sandrock R. 1982a . High - pressure high - temperature differential scanning calorime-ter . Rev Sci Instrum , 53 ( 7 ): 1079 – 1081 .

Sandrock R. 1982b . Differentialkalorimetrie (DSC) bei hohen Dr ü cken; Phasenverhalten und Umwandlungsenthalpien sowie daraus abgeleitete thermodynamische Gr ö en von Polyethylen und Diamantan bis 6000 bar und 600 K. Dissertation, Ruhr - Universit ä t: Bochum.

Schmidt C. , Rittmeier - Kettner M. , Becker H. , Ellert J. , Krombach R. , and Schneider G.M. 1994 . Differential thermal analysis (DTA) and differential scanning calo-rimetry (DSC) at high pressures. Experimental techniques and selected results . Thermochim Acta , 238 : 321 – 336 .

Schneider G.M. 1985 . Recent developments of microcalorimetry at high pressures . Thermochim Acta , 88 : 159 – 168 .

Shulgin A.I. and Godovsky Y. 1992 . DTA measurements on polymers under high pressure — polyethylene and poly(diethylsiloxane) . J Thermal Anal , 38 : 1243 – 1250 .

Szab ó J. , Luft G. , and Steiner R. 1969 . Anwendung der Differentialthermoanalyse zu reaktioiiskinetischen Untersuchungen von Hochdruckreaktionen . Chemie Ing Technik , 41 : 1007 – 101 .

Zhu S. , Bulut S. , Le Bail A. , and Ramaswamy H.S. 2004a . High pressure differential scanning calorimetry (DSC): Equipment and technique validation using water - ice phase - transition data . J Food Process Eng , 27 : 359 – 376 .

Zhu S. , Ramaswamy H.S. , and Le Bail A. 2004b . High - pressure differential scanning calorimetry: Evaluation of phase transitions in pork muscle at high pressures . J Food Proc Eng , 27 : 377 .

Chapter 4

Calorimetry of Proteins in Dilute Solution

G. Eric Plum

67

Introduction 67 Differential Scanning Calorimetry 68 Isothermal Titration Calorimetry (ITC) 77 Conclusion 83 References 84

Introduction

As the food industry begins to take advantage of recent developments in protein chemistry by introducing enzymes and structural proteins into modern food materials and their processing, detailed understand-ing of protein chemical and physical properties becomes increasingly important. Development of a predictive understanding of the energet-ics - structure - function relationships will be required to fully exploit the possibilities presented to engineer proteins with novel substrate speci-fi city or enhanced physical properties, including thermal stability, pH, and ionic strength optima.

Calorimetry provides several valuable tools for the characterization of the thermal properties of proteins and their interactions with other macromolecules and small - molecule affectors. The objective of this chapter is to introduce and summarize the methods of modern


ultrasensitive calorimetry, their application to purifi ed protein samples, and interpretation of the resultant data.

In its broadest terms, calorimetry involves measurement of heat effects in a system in response to some perturbation. Modern ultrasen-sitive calorimetry comprises two distinct techniques, each of which requires specialized instrumentation to affect the perturbation to the system. In differential scanning calorimetry (DSC), that perturbation is a change in temperature of the sample. In isothermal titration calo-rimetry (ITC), the perturbation is the introduction of new material into the sample.

Differential Scanning Calorimetry

Information c ontent To fully exploit the structural and catalytic properties of proteins it is critical to develop a predictive understanding of their functions and stability as a function of temperature and solution conditions. Monitoring the unfolding of a macromolecule induced by exposure to elevated temperature is a classical method for evaluating stability. DSC is particularly well suited to characterization of protein stability because no chromophores are required, nor are optically clear solutions required. Most importantly, interpretation of the resultant data is not dependent on any model of the unfolding process. The thermody-namic characterization of the protein unfolding process derived from DSC data can be used to predict the stability of the protein at any temperature.

With the techniques of modern molecular biology and biochemistry one can manipulate the structure of biological macromolecules almost at will. Site - directed mutagenesis permits substitution or deletion of amino acids at the polypeptide sequence level. Techniques have been developed to include, in addition to the naturally occurring amino acids, a variety of nonnatural amino acid variants into proteins. Because they are state functions, thermodynamic quantities (heat capacity, enthalpy, entropy, free energy) represent sums of contributions from many sources. Systematic comparison of protein variants allows, in principle, for the quantifi cation of contributions of particular amino acid side chain interactions to the measured thermodynamic quantities. The increasingly wide availability of detailed structural models for

Calorimetry of Proteins in Dilute Solution 69

proteins from x - ray crystallography and nuclear magnetic resonance permit one to examine mutation - induced changes in atomic detail. Unfortunately, a simple substitution of one amino acid for another may have effects that propagate well beyond the particular interactions that appear to change in the three - dimensional structure. Because many of the forces that determine the thermodynamic parameters operate on scales of distance smaller than can be reliably determined by the struc-tural methods, these assignments may not be reliable.

Instrumentation During scanning calorimetric examination of a biological macromol-ecule in dilute aqueous solution, most of the energy introduced into the system goes toward heating the solvent. A concentration of about 0.1 mg/ml is typically used for protein DSC. Differential scanning calorimeters of suffi cient sensitivity to study biological macromole-cules in dilute solution typically are based on the power compensation method. Modern ultrasensitive DSC instruments are capable of detecting signals deviating from the baseline of well under 100 nW. While commercial instruments vary in the means by which they make the measurement (Privalov et al. 1995 ; Plotnikov et al. 1997 ), the general concept is described here. The power compensation DSC instrument comprises two matched cells, both fi xed in position and in thermal contact with a thermopile, housed in an adiabatic chamber. In one cell is placed the sample solution. In the second cell is placed a reference solution. A small (2 – 3 atm) pressure is maintained on the cells to suppress bubble formation and evaporation of the samples.

As the instrument is heated, the thermopile responds to differences in temperature of the two cells. Heat is applied to the lagging cell to zero the temperature difference. The amount of energy required to compensate for the thermal event that causes the cell to lag in tempera-ture is directly related to the applied heat, which is quantifi ed in terms of power (energy per time). Thus, the primary data collected by the power compensated DSC instrument is a curve of power versus tem-perature. Division by the heating rate converts the curve to the heat capacity difference between the sample and reference cells, frequently referred to as excess heat capacity, as a function of temperature. Normalization by the amount of analyte yields a curve in terms of specifi c or molar heat capacity.


The sample must be prepared so that the only the difference between the sample and reference solutions is the presence of the macromole-cule of interest. This is generally best accomplished by exhaustive equilibrium dialysis of the sample solution (dialysand) against the buffer solution (dialysate) and subsequent use of the dialysate as the reference solution. Even with the greatest care, the solutions in the sample and reference cells cannot be matched exactly. The macromol-ecule under study displaces solution that contains solvent, buffers, salts, etc., which have temperature - dependent heat capacities. These effects, coupled with imperfect matching of the calorimeter cells, mean that a small but nontrivial baseline correction must be made to separate the contribution of the macromolecule order - disorder transition from the solution displacement and instrumental effects. Small errors in sample preparation and baseline correction can lead to large errors in the calculated thermodynamic parameters. Some attempts have been made to standardize DSC experimental and analysis procedures to reduce interlaboratory variability (Hinz and Schwarz 2001 ), but care should be exercised when comparing data from different laboratories.

Basic e quations With modern instrumentation, complex multidomain DSC thermo-grams can be resolved into individual transitions (V ö lker et al. 1999 ). However, for this discussion it is assumed that the thermogram is of a single transition, which is typically observed for single domain globu-lar proteins in solution. Thus, only monomolecular unfolding pro-cesses, those involving only a single polypeptide chain, are considered. Methods to address multisubunit proteins exist but are beyond the scope of this discussion.

Figure 4.1 shows a simulated DSC thermogram of the temperature - induced unfolding of a small globular protein. Differential scanning calorimetry data are analyzed using a set of standard thermodynamic relations (Privalov and Potekhin 1986 ). All thermodynamic relations herein are at constant pressure. The DSC curve is analyzed in terms of the relationships between the measured heat capacity and the thermo-dynamic parameters of interest.

The excess heat capacity, Cpex, is the difference in heat capacity

between the sample solution containing the macromolecule of interest relative to the reference solution. T m is the temperature at the mid-


point of the transition; that is, the temperature at which the concen-trations of the folded and unfolded forms of the protein are equal. The maximum of the Cp

ex versus temperature curve will correspond to T m only when the unfolding unit is monomolecular and the difference in heat capacity between the folded and unfolded forms of the protein is negligible. The enthalpy change at T m is determined from integration of the DSC curve

ΔH T C Tm p( ) = ∫ exd (4.1)

where the integration covers the entire temperature range of the dena-turation transition. The free energy change at temperature T , which is a measure of the protein ’ s stability at that temperature, depends on the enthalpy and entropy changes.

Δ Δ ΔG T H T T S T( ) = ( ) − ( ) (4.2)

To describe the thermodynamics of the system at any temperature, it is necessary to adjust the DSC determined enthalpy and entropy changes determined at T m . The heat capacity change associated with the order - disorder transition, Δ C p , which comes directly from the DSC curve, is used for the temperature extrapolation.

12,000

10,000C

p c

al m

ol–

1 K

–1 8,000

Tm

ΔH(Tm)

ΔCp

6,000

4,000

2,000

0

300 325 350

Temperature (K)

375 400

Figure 4.1. Simulated DSC thermogram of a small globular protein. The excess heat capacity versus temperature curve is calculated using T m = 350, Δ H ( T m ) = 100 kcal/mol, and Δ C p = 1.5 kcal/mol · K.


Δ Δ ΔH T H T C Tm pT

T

m( ) = ( ) + ∫ d

(4.3)

Δ ( ) = Δ ( ) +

Δ∫S T S T

CT

dTmp

T

T

m (4.4)

For a monomolecular process, Δ G ( T m ) = 0 and therefore from Equation 4.2

Δ ΔS T H T

Tmm

m( ) = ( )

(4.5)

For higher order complexes, additional statistical effects must be included (Marky and Breslauer 1987 ). Assuming that the heat capacity change is independent of temperature, by integrating and combining the expressions above, the enthalpy, entropy, and free energy changes are approximated by

Δ Δ ΔH T H T T T Cm m p( ) ≈ ( ) − −( ) (4.6)

Δ Δ ΔS T S T C T

Tm pm( ) ≈ ( ) − ⎛

⎝⎞⎠ln

(4.7)

Combining Equations 4.2 , 4.6 , and 4.7 permits calculation of the free energy change at any temperature from the three parameters, Δ H ( T m ), T m , and Δ C p , obtainable from the DSC curve.

Δ Δ Δ ΔG T T T

TH T T T C T C T

Tm

mm m p p

m( ) ≈ − ( ) − −( ) + ⎛⎝

⎞⎠ln

(4.8)

Using these expressions one can predict the stability of the protein and its thermodynamic origins at any temperature.

The v an ’ t H off e nthalpy c hange While the enthalpy change measured by DSC does not depend on a model, comparison with models can provide insight into the nature of the order - disorder transition. Consider an equilibrium constant, K eq .

Keq

unfoldedfolded

= [ ][ ]

(4.9)


Because the shape of the DSC curve refl ects the change in the equi-librium constant as a function of temperature, the van ’ t Hoff model can be applied to DSC data as an alternate means of enthalpy change determination. The van ’ t Hoff model is based on the temperature dependence of the dimensionless equilibrium constant.

ΔH RT

KT

RKTvH =

∂∂

⎛⎝⎜

⎞⎠⎟ = −

∂∂( )

⎛⎝⎜

⎞⎠⎟

2

1ln ln

/eq eq

(4.10)

Note that the units of the van ’ t Hoff enthalpy, Δ H vH , are defi ned by the units of the constant R .

Comparison of the model independent calorimetrically determined Δ H ( T m ) value with the Δ H vH value assesses the validity of the assump-tions employed in the derivation of the van ’ t Hoff relation. Specifi cally, it is assumed that the transition from the ordered, low temperature form to the disordered, high temperature form passes through no thermody-namically signifi cant intermediate states (two - state assumption); that is, there is no partial unfolding of the protein in the denaturation pathway. The Δ H vH reports the enthalpy change associated with disrup-tion of a single cooperative unit, the fraction of the protein that acts as a single thermodynamic unit.

There are several methods used to extract Δ H vH from the shape of equilibrium denaturation curves, which are frequently based on indi-rect observations such as temperature - dependent spectroscopic mea-surements (Marky and Breslauer 1987 ). The equation below comes most directly from the DSC curve. Because the equation, as written here, does not account for changes in heat capacity, a DSC curve from which the contribution of Δ C p has been subtracted should be used.

Δ

ΔH RT

CH TvH

p= 4 2max

max

max( ) (4.11)

For small globular proteins, it is expected that Δ H vH = Δ H ( T m ). When Δ H vH ≠ Δ H ( T m ), an error in the determination of the folded protein concentration may be indicated, the baseline may be assigned incor-rectly, or the origins of the deviation may depend on a more funda-mental property of the protein system. While in practice they must be examined and eliminated, for this discussion errors in concentration


determination and assignment of the baseline will be discounted. When Δ H vH < Δ H ( T m ), a deviation from the two - state assumption is indicated; the cooperative unit is smaller than the entire protein. The transition involves either unfolding intermediates or independent domains. When Δ H vH > Δ H ( T m ), aggregation of the unfolded polypeptide has sharpened the DSC observed transition.

Origins of the h eat c apacity c hange The molecular origins of the heat capacity of protein and its change with denaturation are still a matter of active study and debate (Prabhu and Sharp 2005 ). About 30 years ago, Sturtevant (1977) enumerated the underlying contributions to the heat capacity of proteins; these include the hydrophobic effect, electrostatic effects, hydrogen bonds, intramolecular vibrations, and changes in equilibria. The heat capacity and its change with denaturation can be roughly divided into con-tributions from hydration (protein - solvent and solvent - solvent interac-tions) and from intraprotein interactions, Δ Δ ΔC C Cp p p= +Hydration Protein. Relatively little progress has been made in the ensuing years in under-standing the magnitudes of the various contributions to the heat capac-ity. Because it is widely believed that the contribution of hydration dominates, most theoretical and experimental work has been directed at the ΔCp

Hydration term. Record and coworkers (Spolar, Ha, and Record 1989 ) described an

empirical correlation between the change in solvent - accessible nonpo-lar surface area of a protein Δ A np and the heat capacity change associ-ated with thermal denaturation. Numerous workers have extended the empirical model by inclusion of terms to account for differences in the contributions from hydration of polar and nonpolar surface elements (Prabhu and Sharp 2005 ).

Δ Δ ΔC c A c Ap p p np npHydration = + (4.12)

where c p and c np are empirical coeffi cients, Δ A p and Δ A np are the differ-ences between the folded and unfolded forms of the protein in polar and nonpolar surface area in contact with solvent. Although the structure of the folded form of most proteins is stable and the relevant surface areas readily calculated, the fl uctuating structure of the unfolded form is poorly defi ned, and thus different methods of calculating the surface areas of the unfolded form lead to different empirical equations.


Cold denaturation Due to the sign and magnitude of the heat capacity change relative to the changes in enthalpy and entropy observed for globular proteins, at some temperature a maximum in the free energy change is observed (Privalov 1990 ). This further implies that, in addition to the high tem-perature ( T m ) at which Δ G = 0, there is a low temperature that satisfi es the same condition. In most cases, this implied cold denaturation tem-perature is below the freezing point of water; however, there are examples of cold denaturation observable within the aqueous liquid temperature range accessible experimentally with fi xed cell instrumen-tation, wherein the cells must be fi lled completely (Privalov 1990 ). The cold denaturation phenomenon may be particularly important in freez-ing and lyophilization processes.

DSC data can quantify high - affi nity binding Binding equilibria between a protein and a small molecule effector, such as a cofactor or drug, or a second protein subunit, can alter the protein ’ s denaturation temperature. If the second molecule binds more tightly to the folded form of the protein than to the unfolded form, the denaturation will shift to higher temperature. Conversely, preferential binding to the unfolded form shifts the denaturation to lower temperature. DSC thermograms are particularly well suited to measure very tight binding based on the observed binding - induced changes in the heat capacity versus temperature profi les. Brandts and Lin (1990) present methods and models for analysis of the shapes of DSC thermograms to quantify binding affi nities for protein - small molecule and protein - protein interactions up to 10 40 M − 1 , whereas most methods cannot quantify binding constants greater than 10 10 M − 1 .

Assumptions The above described analysis of DSC thermograms in terms of equi-librium thermodynamic parameters depends on a number of assump-tions about the system and the design of the experiment. It is important to evaluate the validity of the assumptions to assure the quality of the derived thermodynamic data and because deviations from the assumed behavior can provide insight into the protein unfold-ing process.


Equilibrium, t wo - s tate, r eversible t ransitions The above analysis of the DSC curve assumes that the system is in equilibrium. The DSC measurement is that of power (energy/time). The observed signal increases with scanning rate; therefore, it is advan-tageous to scan the temperature at a high rate. This advantage is tem-pered by the necessity to maintain equilibrium conditions in the sample. If the scanning rate is too high, the Δ H value will still be correct, but the shape and position of the curve will be compromised. Thus, Δ G, Δ S, and T m will be incorrect. To ensure that the equilibrium assumption is valid, it is advisable to conduct the DSC experiment as a function of scanning rate. If the resulting thermograms are independent of scan-ning rate, the equilibrium assumption is satisfi ed.

As part of the equilibrium assumption, it is further assumed that the process is fully reversible. Generally, protein denaturation is described by a reaction involving a reversible unfolding of the native state ( N ) to form a soluble unfolded form (U), which may subsequently irrevers-ibly form an aggregated state ( D ) (Privalov and Potekhin 1986 ).

N U mD

⎯ →⎯← ⎯⎯

⎯ →⎯

(4.13)

Even if the U → D transition had no effect on C p , it would manifest itself in the shape of the transition, refl ected in an increase in Δ H vH . If the U → D transition is not monomolecular, that is m ≠ 1, Δ H vH will increase with increasing concentration.

It is commonly assumed that subsequent to the temperature induced unfolding process the protein exhibits a random coil confi rmation. This, however, is an oversimplifi cation in most cases. Due to their implication in some diseases, unfolded proteins are receiving intensive structural study (Mittag and Forman - Kay 2007 ). Some proteins refold into an alternate confi rmation, whereas others form aggregates or pre-cipitates. Upon unfolding, most soluble proteins exhibit reduced solu-bility in aqueous solutions and tend to aggregate. The extent of this aggregation varies widely, depending on the specifi c amino acid com-position and sequence as well as the solution conditions. Minor aggre-gate formation may not be readily visualized in the DSC trace but will affect the shape of the transition resulting in a difference between the observed calorimetric and van ’ t Hoff enthalpy changes. Extensive


aggregation resulting in precipitation is often apparent by wild fl uctua-tion in the high temperature baseline.

Concentration and p urity The enthalpy and heat capacity measurements conducted using DSC are based on the amount of material thought to be in the sample. Whether the objective is molar or specifi c values, the measured thermal effects are divided by the quantity of the analyte protein. Therefore, accurate DSC results depend on accurate determination of the amount of analyte protein present, as well as its purity. The best means of determining concentration of the analyte will vary with the properties of the particular protein under study.

It is generally assumed that the concentrations of all components in the solution are suffi ciently low that all activity coeffi cients may be satisfactorily approximated by unity. While this assumption may not be correct, in most cases there is little alternative.

Selection of h ydrogen i on b uffer The selection of hydrogen ion buffer is critically important in DSC experiments. Buffers with high heats of ionization lead to temperature dependent changes in the pH of the solution. Thus, any pH dependent changes in the protein will be superimposed onto the temperature dependent changes, which the DSC experiment is designed to measure. Unfortunately, some of the most widely used buffers for general bio-logical macromolecule studies exhibit high ionization heats. A particu-larly egregious example is tris buffer, although most buffers carrying amine groups are problematic.

A second important issue in buffer selection for DSC studies is the buffer ’ s propensity for metal ion chelation. Because proteins frequently carry anionic functionalities on their surfaces or require metal ion cofactors, interactions with metal ions are often important in stabilizing their structures. Competition for metal ion binding between the protein and the buffer can compromise the DSC experiment.

Isothermal Titration Calorimetry ( ITC )

Information c ontent Most biological processes involve one or more binding events. The types of binding reactions are varied and include, but are not limited


to, assembly of protein subunits into functional enzyme complexes, formation of enzyme - inhibitor complexes, formation of protein - nucleic acid complexes, enzyme - substrate binding, and enzyme - cofactor binding. These binding processes can be described in terms of the standard thermodynamic parameters. A predictive understanding of the binding process can be achieved by measurement of the binding free energy change Δ G , enthalpy change Δ H , entropy change Δ S , and their temperature dependence Δ C p .

All of the binding processes enumerated above are amenable to analysis by some variation of the ITC experiment. Because the binding sites for small molecules to proteins tend to be well defi ned and small in number, and because most of the binding reactions involve a nonzero enthalpy change, protein - small molecule interactions frequently are particularly well suited to examination by isothermal titration calorim-etry. Here, we consider a simple association (without reaction) defi ned by the equilibrium nL + M ↔ ML n of a small molecule, L , with a protein or other macromolecule, M , with n identical, noninteracting binding sites. The ITC method can be applied to more complex equi-libria; however, that is beyond the scope of this discussion.

Instrumentation The design of the power compensation isothermal titration calorimeter is conceptually similar to the power compensation DSC but is adapted for isothermal operation and for introduction of liquid into the sample cell (Wiseman et al. 1989 ). The instrument comprises a thermostated chamber housing inverted lollipop - shaped sample and reference cells. The cell volumes are on the order of 1 ml. A small constant amount of heat is applied to the reference cell. A sensor detects differences in the temperature between the cells, and heat is applied to the lagging cell. The energy applied per unit of time is recorded.

Rather than by heating as in the DSC, the system is perturbed by addition of material into the sample cell by means of a syringe. Usually, the titrant solution contains the small molecule L and the sample cell contains the macromolecule M . Typically, upon introduction of an aliquot of titrant (a few microliters) into the sample cell, an identical volume of the previous solution is expelled from the measuring volume of the cell. Stirring of the sample cell provides effi cient mixing of the titrant solution into the titrate solution. In a typical experiment, about 20 injections of titrant are made. The concentration of L in


the syringe is selected to provide a fi nal ratio of concentrations [ L ]/[ M ] ≈ 2 n .

The primary data from an ITC experiment is a plot of applied power as a function of time. Upon addition of an aliquot of titrant, any thermal response in the sample cell is compensated by heating the sample or reference cell, as appropriate. Suffi cient time must elapse between injections to return the instrument response to the baseline. Thus, for each addition of titrant to the sample cell, a peak is observed in the power - versus - time profi le. Each of these peaks is integrated to yield a value for the thermal response (heat) due to the titrant injection. The resultant plot of the measured heat due to injection of L , d Q /d[ L ], versus the concentration of added binding species, or more typically the molar ratio of the binding species in the cell, that is [ L ]/[ M ], is analyzed to determine the thermodynamic parameters that characterize the binding process. See Figure 4.2 for examples of the raw and inte-grated data plots. For the model described here, the resultant curve is sinusoidal beginning at approximately Δ H for tight binding or less for lower - affi nity binding and declining to a small value that includes dilution effects (see below), with an infl ection point at [ L ]/[ M ] = n .

Thermal effects observed in the ITC experiment include those associated with macromolecule - macromolecule, macromolecule - small molecule interactions, small molecule - small molecule interactions, and heats of dilution, as well as temperature differences between the solu-tion in the syringe and the solution in the sample cell. Therefore, it is critical that additional experiments identical to the fi rst, except for selective absence of the binding species in the sample cell or syringe, be performed to correct for dilution heats and artifacts due to tempera-ture differences between the syringe and sample cell. The corrected heat is determined by subtraction of dilution heats for L and for M as well as a buffer “ dilution ” that corrects for injection artifacts and mis-matched buffers.

Q Q Q Q QL Mcorrected measured _dilution _dilution buffer = − − − (4.14)

ITC d ata a nalysis While in some advanced applications DSC data are model dependent, determination of the basic thermodynamic functions from DSC does not depend on a model of the process under study. In contrast,

Figure 4.2. Isothermal titration calorimetry of binding of two inhibitors to β - glucosidase (Zechel et al. 2003 ) . (a) The raw data (upper panel) and the integrated data with fi tted curve (lower panel) for an inhibitor with K = 1.25 × 10 5 M − 1 . (b) An inhibitor with K = 1.5 × 10 7 M − 1 . Reprinted with permission from J. Am. Chem. Soc. 2003, 125, 14313 – 23. Copyright 2003 American Chemical Society.

80


isothermal titration calorimetry is model dependent. To interpret the ITC curve, expressions must be derived to relate the change in heat, d Q , as a function of change in added small molecule, d[ L ], to the amount of complex formed, d [ LM ], in the cell volume, V .

dQd L

HV d LMd L[ ]

= [ ][ ]

Δ

(4.15)

For the reaction described herein where n = 1, a closed form expression

for ddLML

[ ][ ] can be written in terms of K , [ M ], [ L ] (Wiseman et al.

1989 ).

d LMd L

M KLM

LM

L

[ ][ ]

= +− +

[ ]⎛⎝⎜

⎞⎠⎟ − [ ]

[ ]⎛⎝⎜

⎞⎠⎟

[ ][ ]

⎛⎝⎜

⎞⎠⎟ −

12

1 1 1 2

22 [[ ]

[ ]−

[ ]⎛⎝⎜

⎞⎠⎟ + +

[ ]⎛⎝⎜

⎞⎠⎟

⎛⎝⎜

⎞⎠⎟M M K M K

1 1 1 1 2 12

(4.16)

The ITC curve can then be fi tted by standard nonlinear least squares techniques for Δ H and K . The free energy and entropy changes are determined by the standard relations Δ G ( T ) = RT ln K and Δ Δ ΔS T H T G T

T( ) = ( ) − ( )

. A series of titrations as a function of

temperature provide the heat capacity change associated with the binding reaction via the temperature dependence of the enthalpy

change, Δ ΔC H

Tp = dd

.

Only for the most simple models can a closed form expression be written relating the change in heat accompanying introduction of an aliquot of titrant to the parameters of the binding reaction. More elabo-rate models can be evaluated numerically. Software provided with the instruments can analyze ITC data in terms of several different models, including the single binding site case considered here, multiple identi-cal binding sites that may or may not interact, and multiple classes of distinct binding sites.

Many, if not most, binding events involving macromolecules and small molecules or other macromolecules are coupled to changes in ionization state of one or more charged groups. Thus, the measured


heat associated with the binding event includes the net ionization heats of the groups for which protonation or deprotonation occurs as well as compensating effects from the included hydrogen ion buffer. It is therefore advisable to measure the association heat in two or more hydrogen ion buffers, which differ in ionization heat, at identical pH values (Baker and Murphy 1996 ). The plot of the measured association heat as a function of the buffer ionization heat permits determination of the intrinsic heat of binding and the net number of protons taken up or released upon binding. The intercept at zero buffer ionization heat corresponds to the intrinsic enthalpy of binding; the slope corresponds to the net change in protonation.

Due to the strong model dependence of the ITC method, challenges arise that are not part of the analysis of DSC data. Most ITC - binding isotherms comprise a small number of features: specifi cally, the inter-cept on the enthalpy axis and one or two infl ection points. Thus, the number of fi tting parameters that the data can support is limited. Many processes of interest involve competing equilibria with multiple binding species and binding sites that require elaborate multiparameter models to describe. Caution, and appropriate statistical tests, should be applied to ensure that the data can support the applied model. It is relatively easy to devise a model that cannot be adequately addressed by ITC isotherms alone; however, if complementary data are available to fi x some of the fi tting parameters, such models may become tenable.

Range of a pplicability The classical ITC experiment described here is limited to binding affi nities and concentrations that produce a titration curve with a shape that can be fi t accurately by nonlinear least squares methods. A rough estimate of this range defi ned by the macromolecule concentration and the equilibrium association constant is 1 < [ M ] K < 1000 (Wiseman et al. 1989 ), with 10 < [ M ] K < 100 being preferred. Note that while the macromolecule concentration could always be adjusted to place [ M ] K in range, the heat produced by the binding reaction must be detectable. This places an effective limit on the range of binding affi nities acces-sible to direct ITC measurement.

Figure 4.3 shows how the shape of the ITC titration curve varies with [ M ] K . As [ M ] K → ∞ , the ITC curve becomes a step function with essentially all the L injected binding to M , until all of the available binding sites are exhausted. So d Q /d[ L ] = Δ H for [ L ]/[ M ] < n and


d Q /d[ L ] = 0 for [ L ]/[ M ] > n . There is no information about K except that it is large. As [ M ] K → 0, the ITC curve approximates a fl at line and provides no information on Δ H , n , or K.

Linkage to other equilibria can be used to measure indirectly binding affi nities that are too tight or too weak to measure by standard ITC experiments (Doyle et al. 1995 ; Sigurskjold 2000 ). If n is known, reasonable estimates of K may be obtained by extension of the titration range so the fi nal [ L ]/[ M ] >> 2 n (Turnbull and Daranas 2003 ). Note that this method provides only an estimate of K and will not provide accurate Δ H values.

Conclusion

Modern ultrasensitive calorimetry provides powerful tools for under-standing the stability of proteins in solution, the forces that maintain their folded structures, and their interactions with other macromole-cules and small molecules.

Differential scanning calorimetry quantifi es the thermal ( T m ) and thermodynamic ( Δ G ) stabilities of the protein. The thermodynamic origins of the stability ( Δ H , Δ S , and Δ C p ) can be interpreted to dissect the forces maintaining the folded structure and how they depend on

Figure 4.3. Dependence of the shape of the ITC titration curve on [ M ] K for the reac-tion nL + M ↔ ML n .


the structure of the protein and the solution conditions. The model - independent values derived directly from the DSC thermogram can be compared to model - dependent values derived from the shape of the DSC curves to gain insight into the unfolding mechanism and aggregation.

Isothermal titration calorimetry provides a means to directly measure the heat of interaction ( Δ H ) of a protein with another macromolecule or with small molecules. Application of models for the association reaction provides detailed thermodynamic characterization ( K , Δ G , Δ S , and Δ C p ) of the binding process.

Taken together, the techniques of modern solution calorimetry provide a predictive understanding of the stability of a protein and its interactions with other molecules as a function of temperature and solution conditions.

References

Baker B.M. and Murphy K.P. 1996 . Evaluation of linked protonation effects in protein binding reactions using isothermal titration calorimtery . Biophys J , 71 : 2049 – 55 .

Brandts J.F. and Lin L. 1990 . Study of strong to ultra tight protein interactions using differential scanning calorimetry . Biochemistry , 29 : 6927 – 40 .

Doyle M.L. , Louie G. , Dal Monte P.R. , and Sokoloski T.D. 1995 . Tight binding affi nities determined from thermodynamic linkage to protons by titration calorim-etry . Methods Enzymol , 259 : 183 – 94 .

Hinz H.J. and Schwarz F.P. 2001 . Measurement and analysis of results obtained on biological substances with differential scanning calorimetry . Pure Appl Chem , 73 ( 4 ): 745 – 59 .

Marky L.A. and Breslauer K.J. 1987 . Calculating thermodynamic data for transitions of any molecularity from equilibrium melting curves . Biopolymers , 26 : 1601 – 20 .

Mittag T. and Forman - Kay J.D. 2007 . Atomic - level characterization of disordered protein ensembles . Curr Opin Struct Biol , 17 : 3 – 14 .

Plotnikov V.V. , Brandts J.M. , Lin L. , and Brandts J.F. 1997 . A new ultrasensitive scanning calorimeter . Anal Biochem , 250 : 237 – 244 .

Prabhu N.V. and Sharp K.A. 2005 . Heat capacity in proteins . Annu Rev Phys Chem , 56 : 521 – 48 .

Privalov G. , Kavina V. , Freire E. , and Privalov P.L. 1995 . Precise scanning calorim-eter for studying thermal properties of biological macromolecules in dilute solu-tion . Anal Biochem , 232 : 79 – 85 .

Privalov P.L. 1990 . Cold denaturation of proteins . Crit Rev Biochem Mol Biol , 25 ( 4 ): 281 – 305 .

Privalov P.L. and Potekhin S.A. 1986 . Scanning microcalorimetry in studying tem-


perature - induced changes in proteins . Methods Enzymol , 131 : 4 – 51 . Sigurskjold B.W. 2000 . Exact analysis of competition ligand binding by displacement

isothermal titration calorimetry . Anal Biochem , 277 : 260 – 66 . Spolar R.S. , Ha J. , and Record , Jr. M.T. 1989 . Hydrophobic effect in protein folding

and other noncovalent processes involving proteins . Proc Natl Acad Sci USA , 86 : 8382 – 85 .

Sturtevant J.M. 1977 . Heat capacity changes in processes involving proteins . Proc Natl Acad Sci USA , 74 : 2236 – 40 .

Turnbull W.B. and Daranas A.H. 2003 . On the value of c: Can low affi nity systems be studied by isothermal titration calorimetry? J Am Chem Soc , 125 : 14859 – 66 .

V ö lker J. , Blake R.D. , Delcourt S. G. , and Breslauer K.J. 1999 . High - resolution calo-rimetric and optical melting profi les of DNA plasmids: Resolving contributions from intrinsic melting domains and specifi cally designed inserts . Biopolymers , 50 : 303 – 18 .

Wiseman T. , Williston S. , Brandts J.F. , and Lin L. 1989 . Rapid measurement of binding constants and heats of binding using a new titration calorimeter . Anal Biochem , 179 : 131 – 7 .

Zechel D.L. , Boraston A.B. , Gloster T. , Boraston C.M. , Macdonald J.M. , Tilbrook D. , Matthew G. , Stick R.V. , and Davies G.J. 2003 . Iminosugar glycosidase inhibi-tors: Structural and thermodynamic dissection of the binding of isofagomine and 1 - deoxynojirimycin to β - glucosidases . J Am Chem Soc , 125 : 14313 – 23 .

Chapter 5

Thermal Analysis of Denaturation and Aggregation of Proteins and Protein Interactions in a Real Food System

Valerji Y. Grinberg , Tatiana V. Burova , and Vladimir B. Tolstoguzov

87

Introduction 87 Effects of pH on Thermal Denaturation of Food Proteins 89 Effects of Salts on Thermal Denaturation of Food Proteins 95 Effects of Alcohols on Thermal Denaturation of Food Proteins 99 Effects of Odorants on Thermal Denaturation of Food Proteins 102 Effects of Polysaccharides on Thermal Denaturation of Food

Proteins 105 Postdenaturation Aggregation of Food Proteins 110 Conclusion 112 References 113

Introduction

Normally, food contains a heterogeneous, heterophase mixture of high - and low - molecular - weight components and their aggregates and complexes. Among food macromolecules, proteins and polysaccha-rides are largely responsible for the structural changes accompanying food processing and for mechanical and other physical properties of foods.

Proteins are both most - multifunctional biopolymers and most - versatile food macromolecules. Normally, a functional protein has a unique ordered (crystal - like) molecular structure, which appears to be


responsible for a high specifi city and effi ciency of its functioning. Food proteins greatly infl uence the structure - property relationship in foods.

Heating is one of the most important treatments of food processing. Heat denaturation and aggregation of proteins are therefore the most typical events in food processing. For instance, the difference between raw and boiled eggs is caused by the denaturation and aggregation of the denatured egg proteins. The heat denaturation involves a coopera-tive or noncooperative transition of a protein from its folded to its unfolded state. It is related to some structural disorganization of the three - dimensional structure of native molecules. The unfolding changes the interaction of the protein with aqueous medium and induces aggre-gation of the unfolded protein molecules. Consequently, the denatur-ation governs the structure, fl avor, texture, and other qualities of food. It also contributes to the nutritional qualities and physical stability of the foods during storage.

The heat effects of denaturation and aggregation of proteins are usually small and have the opposite sign, namely, heat absorption (endothermic) and release (exothermic), respectively. The key role of heating in food processing determines a high effi ciency of thermal analysis techniques, in particular, differential scanning calorimetry (DSC), for food system investigations. Among various DSC methods, the high - sensitivity differential scanning calorimetry is of most signifi -cance. It was developed and substantially evaluated in the USSR Academy of Sciences and later extensively applied and improved in many laboratories. Its high sensitivity provides the heat capacity mea-surements in dilute solutions of proteins and other biopolymers. In the fi eld of food science, the high - sensitivity DSC was fi rst systematically applied at the A.N. Nesmeyanov Institute of Organo - Element division of the USSR Academy of Sciences in the early 1980s (Tolstoguzov et al. 1985 ; Tolstoguzov 1988 , 1991 ; Grinberg et al. 1989 ). A system-atic investigation in the fi eld of food protein denaturation was begun with thermal denaturation of individual proteins under physicochem-ical conditions typical of food processing (Grinberg et al. 1988 , 1989 , 2000 ; Burova et al. 1989a , b ; Burova et al. 1991 ). These conditions are pH, salt composition (Bikbov et al. 1983 ; Danilenko et al. 1986a , 1987 ), the presence of lipids and fl avor compounds (Mikheeva et al. 1998 ; Burova et al. 1999 ; Grinberg et al. 2002 ; Burova et al. 2003 ), other low - molecular weight compounds (Danilenko et al.

Thermal Analysis of Denaturation and Aggregation 89

1986b ), and polysaccharides (Tolstoguzov 1988 , 1991 ; Burova et al. 1992a , 2002b ). The applied objective of calorimetry was to elucidate the mechanisms of structure formation and structure - texture and struc-ture - physical property relationships in foods.

These investigations resulted in (1) general methodological approaches to study denaturation of proteins; (2) basic information on the protein behavior upon heating and processing into different kinds of food; (3) food fl avorings; (4) thermodynamic compatibility of denatured proteins with native proteins and polysaccharides and phase behavior of macromolecular components in biological and food systems; and (5) thermodynamic aspects of composition - property rela-tionship in formulated food (Tolstoguzov 1988 , 1998 , 2000 , 2002 ). The results have been reported in many reviews and research publica-tions (Tolstoguzov et al. 1985 ; Tolstoguzov 1988 , 1991 , 2000 , 2002 ; Grinberg et al. 1989 , 2000 ; Burova et al. 2003 ).

This chapter is concentrated on the effects of pH, neutral salts, alcohols, and polysaccharides on thermal denaturation of food pro-teins. Another objective of this chapter is to consider the potential of high - sensitivity DSC for investigation of protein aggregation.

Effects of p H on Thermal Denaturation of Food Proteins

Among food proteins, the seed storage oligomeric proteins and, pri-marily, 11S globulins that represent the main proteins of most oil and legume seeds, are of particular importance. The molecule of 11S glob-ulins has a molecular weight of about 300 kDa and consists of six subunits located in the vertices of a trigonal antiprism. Each subunit contains two polypeptide chains linked by a disulfi de bond. Upon thermal denaturation, the molecule of 11S globulin behaves as an ensemble of 12 independent cooperative units (domains). The unfold-ing mechanism of domains is consistent with the two - state model (Grinberg et al. 1988 ). This two - state model can therefore be used to analyze the denaturation of 11S globulins.

The quaternary structure of 11S globulins depends on pH. The pH - induced changes in the quaternary structure of 11S soybean globulin (taken as an example) were compared with changes in conformational stability of the protein (Danilenko et al. 1987 ).


The sedimentation velocity data show that at room temperature 11S globulin undergoes the following structural changes upon decreas-ing pH from 7.6 to 2.0: 11S (dodecamer) ↔ 7S (hexamer) → 3S (dimer).

The thermograms of 11S globulin at different pH values are given in Figure 5.1 a. With deceasing pH, the denaturation heat capacity peak splits into two peaks that are shifted to lower temperatures. The high - temperature peak decreases, and the low - temperature peak increases; only the low - temperature peak is observed at pH 3.0. The 11S globulin converts into the 3S dimer at pH ≤ 2.75, and the thermograms do not display any peaks. Hence, the low - temperature and high - temperature peaks in the bimodal thermograms of 11S globulin can be assigned to the denaturation of the hexamer and the dodecamer, respectively.

Figure 5.1 b shows that the denaturation temperatures of both forms of 11S globulin decrease with decreasing pH.

A deconvolution of the thermogram of 11S globulin can be per-formed in terms of the two - state model if one considers the dodecamer and the hexamer as ensembles of 12 and 6 identical cooperative units, respectively. At pH 3.5, it provides values of the denaturation enthalpy and heat capacity increment for both forms of the protein: Δ d H 7 S = 12.2 ± 0.5 J g − 1 , Δ d C p ,7 S = 0.50 ± 0.08 J g − 1 K − 1 and Δ d H 11 S = 20.7 ± 1.5 J g − 1 , Δ d C p ,7 S = 0.46 ± 0.08 J g − 1 K − 1 .

A difference in the denaturation heat capacity increments does not seem to be signifi cant. However the denaturation enthalpy of the dodecamer is signifi cantly larger than that of the hexamer. This differ-ence refl ects the dependence of the denaturation enthalpy on tempera-ture. According to Kirchhoff ’ s law, it is a linear function with a slope

Figure 5.1. Thermal denaturation of 11S soybean globulin at different pH values. (a) Thermograms at different pH values (shifted arbitrary along the heat capacity axis). Points represent approximation of the thermogram at pH 7.6 by a two - state model considering the protein molecule as an ensemble of 12 thermodynamically equivalent domains. (b) Denaturation temperatures of dodecamer and hexamer forms of the protein versus pH. The insert shows the pH dependences of weight fractions of the dodecamer and hexamer forms, 11S and 7S, respectively, from velocity sedi-mentation data. (c) Correlation between values of the denaturation enthalpy and the denaturation temperature for different forms of the protein according to Kirchhoff ’ s law (see explanations in the text). (d) Excess denaturation free energy per constituent chain of the protein versus pH at temperature T 0 = 352 K.

91


equal to the denaturation heat capacity increment, Δ d C p (Privalov 1979 ). Figure 5.1 c demonstrates a correlation between the denaturation enthalpy and the denaturation temperature of the different forms of 11S globulin at pH 3.5 and pH 7.6. This dependence is approximated by a straight line with a slope of 0.46 ± 0.10 J g − 1 K − 1 . It coincides with the average value of the denaturation heat capacity increment of 11S globulin, Δ c C p = 0.46 ± 0.04 J g − 1 K − 1 . This result reveals a thermody-namic consistency of the denaturation parameters of 11S globulin.

The denaturation parameters of the 11S globulin forms can be used to calculate their denaturation free energies at a standard temperature as a function of pH (Prigogine and Defay 1954 ):

Δ Δ Δ

Δ

d dd

d p d

d p

G T H TT

C T T

T C

00

0 0

0

1, pH pHpH

pH( ) = ( ) −( )

⎡⎣⎢

⎤⎦⎥

+ − ( )[ ]−

lln TTd

0

pH( )⎡⎣⎢

⎤⎦⎥

(5.1)

Here, the standard temperature T 0 = 352 K is the denaturation tempera-ture at a reference value of pH (pH 0 3.5).

The excess denaturation free energy, that is, a general criterion of pH effects on protein denaturation, can then be determined:

Δ Δ ΔdE

d dG G T G TpH pH pH( ) = ( ) − ( )0 0 0, , (5.2)

Dependences Δ c G E (pH) of the dodecamer and hexamer forms of 11S globulin seem to be in close agreement and can be approximated by a single straight line (Figure 5.1 d). According to the two - state model (Ptitsyn and Birstein 1967 ), a slope of this line is directly linked to a change in the number of the protein - bound protons upon denaturation, Δ d ν H+ :

1 2 303RT

Gd E

Td

∂∂

⎛⎝⎜

⎞⎠⎟ = ×Δ Δ

pHH+. ν

(5.3)

In the case of 11S globulin, Δ d ν H+ ≅ 3 per each constituent polypep-tide chain. This value is close to the similar estimates for small globular proteins (Tanford 1968 , 1970 ; Nicoli and Benedek 1976 ).

The origin of the denaturation proton adsorption by proteins at acid pH values is well - known (Tanford 1968 , 1970 ). In the native protein


molecule, there are often abnormal carboxyl groups with pK a ≅ 1.5 (Nicoli and Benedek 1976 ) localized in the nonpolar interior of the molecule. Their ionized state is stabilized by hydrogen bonds with neighboring residues of tyrosine, lysine, or histidine. Several of these bonds decrease with decreasing pH, which leads to a decrease in the conformational stability of the protein globule. In the course of the denaturation, the hydrogen bonds tyrosyl (histidyl) - carboxylate are broken, and the abnormal carboxyl groups become normal groups with pK a 3 to 4. They turn to the nonionized state by the adsorption of protons. Typically, several of the abnormal carboxyl groups in the protein molecule are rather small; for example, they do not exceed 2 to 3 for some small globular proteins (Nicoli and Benedek 1976 ).

The considered approach was used to analyze the pH effects on the stability of a number of other oligomeric and small food proteins, such as ribulose 1,5 biphosphate carboxylase (RBPC) of alfalfa (Burova et al. 1989B ), tobacco (Burova et al. 1991 ), and other green leaves; 7S globulin of French beans (Burova et al. 1989b , 1992 ); soybean trypsin Kunitz inhibitor (Varfolomeeva et al. 1989 ; Burova et al. 1990 ; Grinberg et al. 2000 ); and porcine β - lactoglobulin (Burova et al. 2002a ).

The 7S globulin of French beans, phaseolin, is an oligomeric storage protein with the molecular weight of about 150 kDa. It contains three subunits (Paaren et al. 1987 ). Each subunit involves two domains (Lawrence et al. 1990 ). The thermal denaturation of 7S globulin was studied in the range of pH 2.0 to 10.9 (Burova et al. 1992 ). The qua-ternary structure of the protein is stable within this pH range; however the denaturation thermogram of phaseolin has a complex profi le. In addition to the main heat capacity peak, there is a lower temperature shoulder. The thermogram recalculated per polypeptide chain can be deconvoluted into two independent two - state transitions that represent unfolding of the domains of phaseolin. Dependences of the denatur-ation temperature and enthalpy of both domains on pH pass through a maximum at pH 5.4. The latter corresponds to the isoelectric point of phaseolin. For both domains, the temperature dependences of the dena-turation enthalpies strictly follow the Kirchhoff ’ s law. The excess denaturation free energies of the domains are maximal in the vicinity of the isoelectric point of phaseolin. The analysis of the excess dena-turation free energies of the domains as a function of pH showed that there are at least two types of the side - chain hydrogen bonds:


tyrosyl - carboxylate and histidyl - carboxylate. There are six bonds in the high - temperature domain and four in the “ low - temperature ” domain. All hydrogen bonds are presumably localized in the hydro-phobic interiors of the domains.

The Kunitz inhibitor (KI) is one of several trypsin inhibitors of soybean (Kunitz 1947 ; Koshiyama et al. 1981 ). It is a small globular protein with the molecular weight of 21.5 kDa (Wu and Scheraga 1962 ; Koide and Ikenaka 1973 ) belonging to globulins. Its polypeptide chain consists of 181 amino acid residues and contains two disulfi de bonds. A remarkable feature of this protein is a very low rate of the thermal denaturation (Kunitz 1948 ). For example at 45 ° C, a time of the half – conversion of the denaturation process is about 6 h, that is, of 1 to 2 orders of magnitude longer than that of other globular proteins (Joly 1965 ). The thermal denaturation of KI was studied in the range of pH 2 – 12 (Varfolomeeva et al. 1989 ; Burova et al. 1990 ; Grinberg et al. 2000 ). Due to the slow denaturation rate of KI, the heating rate affects calorimetric curves within a wide region of pH values. An increase in the heating rate from 0.01 up to 2.0 K min − 1 increases the apparent denaturation temperature by about 20 ° C without any signifi cant changes in the denaturation enthalpy and heat capacity increment. It is important that an increment of the apparent denaturation temperature induced by pH alteration does not depend on the heating rate. This allows one to calculate the denaturation free energy of KI as a function of pH. The dependence of the true thermodynamic denaturation tem-perature on pH has a rather wide plateau at pH 4.4 – 8.0 and rapidly decreases at both lower and higher pH values. It is important that a point of the maximal conformational stability of KI (about pH 7) does not coincide with its isoelectric point (pH 4.5). It means that a contri-bution of electrostatic effects to the conformational stability of KI is rather small. The pH dependence of the excess denaturation free energy of KI shows that the native protein molecule contains two pH - sensitive side - chain hydrogen bonds of the tyrosyl - carboxylate and lysyl - carboxylate types.

Bovine β - lactoglobulin is one of the most important milk proteins (Hambling et al. 1992 ). Its thermal behavior, including the thermal denaturation and postdenaturation aggregation, is of signifi cance for many food technologies (Relkin 1996 ; Holt 2000 ). The presence of the free thiol (Cys121) is a structural feature of bovine β - lactoglobulin. This functional group of high reactivity to thiol - disulfi de exchange


becomes accessible in the course of the thermal denaturation and induces the postdenaturation aggregation (Burova et al. 1998 ). Porcine β - lactoglobulin is a close homolog of bovine β - lactoglobulin (66% identity of their primary structures), but it does not contain a free thiol group (Hoedemaeker et al. 2002 ). It was therefore of interest to compare the thermal denaturation behavior of porcine and bovine β - lactoglobulins (Burova et al. 2002a ).

The thermal denaturation of porcine β - lactoglobulin is reversible at pH 2 – 10, while that of bovine β - lactoglobulin is reversible only below pH 3.5. This difference supports the assumed postdenaturation aggregation of β - lactoglobulin initiated by the free accessible thiol in the unfolded protein. The aggregation is responsible for irreversibility of the thermal denaturation of bovine β - lactoglobulin. The denatur-ation temperature and enthalpy of porcine β - lactoglobulin are maximal at a pH of about 6.5. With increasing or decreasing pH relative to pH 6.5, both denaturation parameters decrease, and more rapidly in the acid region. The maximal stability of bovine β - lactoglobulin coin-cides with its isoelectric point (pH ∼ 4.5), diminishes upon increase and decrease in pH values, and does so more rapidly in the alkaline region. Thus the denaturation parameters of bovine β - lactoglobulin exceed the denaturation parameters of porcine β - lactoglobulin in the acid region and are signifi cantly lower within the alkaline region. The analysis of pH dependence of the excess denaturation free energy of both proteins shows their considerable difference in the confor-mational stability. The latter seems to refl ect the different role of carboxyl groups in the formation of pH - sensitive hydrogen bonds stabilizing the native proteins. These carboxyl groups are proton donors in bovine β - lactoglobulin and proton acceptors in porcine β - lactoglobulin.

Effects of Salts on Thermal Denaturation of Food Proteins

Normally, the effects of salts on thermal denaturation of food protein corresponds to the Hoffmeister lyotropic series (Danilenko et al. 1986a ; Yamasaki et al. 1991 ; Komsa - Penkova et al. 1996 ; Pico 1996 ; Kim et al. 2004 ). The kosmotropic salts ( “ salting - out ” salts) increase the dena-turation temperature of protein, whereas chaotropic salts ( “ salting - in ” salts) decrease it.


Effects of neutral salts on the protein thermal denaturation were studied using 11S globulin of broad beans as an example (Danilenko et al. 1986A ).

Figure 5.2 a shows thermograms of the 11S globulin at different NaCl concentrations. When the salt concentration increases, the dena-turation heat capacity peak shifts to higher temperatures, increases in height, and becomes narrower. Both the denaturation temperature and enthalpy increase with increase in the salt concentration (Figure 5.2 b). There is a strict linear correlation between values of the denaturation enthalpy and the denaturation temperature obtained at different salt concentrations (Figure 5.2 c). Its slope, 0.42 ± 0.02 J g − 1 K − 1 , agrees well with the denaturation heat capacity increment, 0.40 ± 0.02 J g − 1 K − 1 , determined directly in accordance with the Kirchhoff ’ s law.

A general criterion of salt effects on protein denaturation is the excess denaturation free energy:

Δ Δ ΔsE

s d s dG C G T C G T( ) = ( ) − ( )0 0 0, , (5.4)

where C s is the salt concentration and T 0 is the reference temperature. It can be calculated as a function of the salt concentration at the refer-ence temperature by Equation 5.1 using experimental values of the denaturation temperature, enthalpy, and heat capacity increment. The excess denaturation free energy of 11S globulin is plotted against the NaCl concentration in Figure 5.2 d. It is positive and increases with increasing salt concentration. Thus the salt enhances the conforma-tional stability of 11S globulin.

The salt - induced changes in the conformational stability of 11S globulin are determined by the effect of screening of electrostatic interactions of surface charges of the native ( N ) form of the protein

Figure 5.2. Thermal denaturation of 11S broad bean globulin in the presence of sodium chloride at pH 7.6. (a) Thermograms at the different salt concentrations, C s . (b) Denaturation temperature and enthalpy versus the salt concentration. (c) Correlation between values of the denaturation enthalpy and the denaturation temperature obtained at the different salt concentrations, according to Kirchhoff ’ s law. The slope of the correlation line coincides with the denaturation heat capacity increment, Δ d C p , deter-mined directly. (d) Excess denaturation free energy per constituent chain of the protein versus NaCl concentration at temperature T 0 = 352 K. The solid line curve is calculated by a two - state model, taking into account the electrostatic screening and lyotropic effects.

97


and by the lyotropic effect of the salt on the structure of water. The excess denaturation free energy can be determined as a sum of contri-butions of these two effects:

Δ Δ ΔdE

s d eE

s d sE

sG C G C G C( ) = ( ) + ( ) (5.5)

where Δ ( )d eE

sG C and Δ ( )d sE

sG C are the contributions of the electro-static and lyotropic effects, respectively. The contribution of the elec-trostatic effect can be expressed in the Debye - H ü ckel approximation (Tanford 1965 ):

Δ ( ) = × ( ) − ( )[ ]d eE

s eG C RT B F Fκ κ0 (5.6)

where

F n

RNκ κ

κ( ) = ×

+2 1 (5.7)

and B e is the electrostatic interaction parameter; n = 12 is the number of constituent chains of 11S globulin; κ = κ ( C s ) and κ 0 = κ ( C s = 0) are the values of the Debye - H ü ckel parameter; R N ≅ 5 nm is the molecular radius of 11S globulin. The contribution of the lyotropic effect can be considered in the form (Schellman 1978 ) equivalent to the Setschenow equation (Setschenow 1889 ):

Δ Δd sE

d s sG RT B C= × (5.8)

where Δ d B s is the difference in the second virial coeffi cients of the unfolded ( D ) and native ( N ) forms for salt - protein interactions.

Equations 5.5 to 5.8 describe well the dependences of the excess denaturation free energy of 11S globulin on salt concentration for NaCl, KCl and (NH 4 ) 2 SO 4 (for example, see Figure 5.2 d). The deter-mined parameters B e and Δ d B s are presented in Table 5.1 .

The values of the parameter B e correspond to an apparent 11S globu-lin charge of 4 – 6 proton units per constituent chain. Note that many globular proteins carry about the same charge in the vicinity of the iso-electric point (Tanford 1965 ). The parameter B e for (NH 4 ) 2 SO 4 is larger than that for NaCl. It refl ects an increase in the protein charge due to more strong binding of sulfate anions to the protein (Record et al. 1978 ).


According to the obtained Δ d B s values, the salts can form a series (NH 4 ) 2 SO 4 >> NaCl = KCl in agreement with general features of the lyotropic effect (von Hippel and Schleich 1969 ).

It is possible to calculate the parameter Δ d B s by the group contribu-tion method (Nandi and Robinson 1972 ) and the Melander - Horvath theory (Melander and Horvath 1977 ). In both cases, the theoretical estimates of the parameter Δ d B s of 11S globulin are very close to its experimental values (Table 5.1 ).

Effects of Alcohols on Thermal Denaturation of Food Proteins

Effects of alcohols on thermal denaturation of food proteins can be of importance for food technology, specifi cally, for control of functional properties of food proteins (Grozav et al. 1985 ). Generally, alcohol can decrease the denaturation temperature and, consequently, the protein conformational stability (Grozav et al. 1985 ; Danilenko et al. 1986b ; Stepuro et al. 1991 ; Cinelli et al. 1997 ; Grinberg et al. 1998 ; van Koningsveld et al. 2002 ; Michnik 2007 ). It is, however, noteworthy that an extrapolation to low temperatures of calorimetric data on thermal protein denaturation reveals that the presence of low alcohol concentra-tions can stabilize the native conformation of protein (Danilenko et al. 1986B ). This effect can be undoubtedly of interest for food storage.

The most detailed information about alcohol effects on thermal denaturation of food proteins was obtained for the 11S globulin from broad beans (Danilenko et al. 1986b ).

In aqueous ethanol solutions, the denaturation heat capacity peak of 11S globulin decreases, broadens, and moves to lower temperatures

Table 5.1. Salt - protein interaction parameters B e and Δ d B s for 11 S globulin from broad beans.

Salt 10 6 B e , cm

Δ d B s , L mol − 1

Experimental Group contribution method

Melander - Horvath theory

NaCl 1.2 8.2 8.4 8.8 KCl 1.2 8.2 — 7.8 (NH 4 ) 2 SO 4 2.3 10.6 — 11.0


(Figure 5.3 a). A new, small high - temperature peak is resolved at the ethanol concentrations higher than 0.5 M. It can be assumed that this peak is associated with a molten globule - coil transition, since it has shown a signifi cant increase in lifetime of the molten globule structure of proteins in the presence of alcohols (Biringer and Fink 1982 ; Burova et al. 2000 ). The denaturation temperature and enthalpy decrease lin-early with the ethanol concentration (Figure 5.3 b). The denaturation heat capacity increment does not depend practically on the ethanol concentration. Its average value amounts to 0.31 ± 0.02 J g − 1 K − 1 . The dependence of the denaturation enthalpy on the denaturation tempera-ture is generally in accordance with the Kirchhoff ’ s law (Figure 5.3 c).

Figure 5.3 d shows the dependence of the excess denaturation free energy of 11S globulin on the ethanol concentration. It can be approxi-mated by a linear function that is convenient to represent in a form equivalent to the Setschenow equation:

Δ ΔdE

d A AG RT B C= × (5.9)

where Δ d B A is the difference in the second virial coeffi cients of the D and N forms of the protein for alcohol - protein interactions and C A is the molar ethanol concentration. Here, Δ d B A = − 3.46 ± 0.06 L mol − 1 . Hence, ethanol considerably decreases the conformational stability of 11S globulin in the temperature range, where the thermal denaturation can be observed.

Figure 5.3. Thermal denaturation of 11S broad bean globulin in the presence of ethanol at pH 7.6. (a) Thermograms at the different alcohol concentrations, C A . (b) Denaturation temperature and enthalpy versus the alcohol concentration. (c) Correlation between values of the denaturation enthalpy and the denaturation tem-perature obtained at the different alcohol concentrations according to Kirchhoff ’ s law. The solid line corresponds to the average value of the experimental denaturation heat capacity increment, Δ d C p = 0.31 ± 0.02 J g − 1 K − 1 . The dashed lines represent a predic-tion range of the correlation. (d) Excess denaturation free energy per constituent chain of the protein versus ethanol concentration at temperature T 0 = 352 K. The line is calculated by a two - state model using the linear approximation of alcohol - protein interactions in terms of the denaturation increment of the second virial coeffi cient, Δ d B A . The insert gives a correlation between values of Δ d B A determined experimen-tally and those calculated by an additive scheme based on independent group contri-butions of amino acids for lysozyme, ribonuclease, and 11S broad bean globulin. The correlation coeffi cient is more than 0.999.

101


Effects of Odorants on Thermal Denaturation of Food Proteins

Binding of odorants to proteins is one of the key factors of food fl avor-ing. Normally, binding of odorant is due to its hydrophobic interactions with the accessible apolar groups of the protein. Consequently, a close link should exist between the odorant affi nity to the protein and the conformational state of the protein. Odorant - protein interactions could therefore be studied using the effect of odorant binding on thermal denaturation of proteins (Burova et al. 1999 , 2003 ; Grinberg et al. 2002 ).

Figure 5.4 a shows that vanillin decreases, broadens, and moves the denaturation heat capacity peak of ovalbumin to lower temperatures (Grinberg et al. 2002 ). The denaturation temperature and enthalpy of the protein decrease linearly with the vanillin concentration (Figure 5.4 b). Since the thermal denaturation of ovalbumin is a nonequilibrium process, its denaturation parameters determined by DSC depend on the heating rate. The kinetic factor complicates interpretation of the calo-rimetric data on the ovalbumin thermal denaturation. Nevertheless, Figure 5.4 c shows that there is a good linear correlation between values of the denaturation enthalpy and the denaturation temperature deter-mined at different heating rates, pH values, and vanillin concentrations. Its slope is close to the denaturation heat capacity increment of oval-bumin (Sochava and Smirnova 1993 ). This is a rare example of validity of the thermodynamic Kirchhoff ’ s law concerning the nonequilibrium data.

Figure 5.4. Thermal denaturation of ovalbumin in the presence of vanillin. (a) Thermograms at the different vanillin concentrations, L , at pH 6.7. (b) Denaturation temperature and enthalpy versus vanillin concentration at pH 6.7. (c) Generalized correlation between values of the denaturation enthalpy and the denaturation tempera-ture obtained at different vanillin concentrations (0 – 75 mM), heating rates (0.125 – 2.0 K min − 1 ), and pH values (pH 3.0 and pH 6.7). The slope of the correlation line, 0.43 ± 0.1 J g − 1 K − 1 , is close to the denaturation heat capacity increment of ovalbumin (Sochava and Smirnova 1993 ). The dashed lines represent a prediction range of the correlation. (d) Excess denaturation free energy of ovalbumin versus the vanillin concentration at pH 3.0 ( T 0 = 333.4 K) and pH 6.7 ( T 0 = 351.2 K). The solid line curves are calculated by a model of ligand binding to polymer matrix with indepen-dent identical sites at the following binding parameter values: the denaturation incre-ment of the number of binding sites, Δ d ν = 3.0 ± 0.1 (pH 3.0) and 20.0 ± 0.1 (pH 6.7); the binding constant, K b = 32.9 ± 1.3 M − 1 (pH 3.0) and 5.3 ± 0.1 M − 1 (pH 6.7).

103


The Lumry - Eyring model (Lumry and Eyring 1954 ) can be applied for estimation of equilibrium values of the denaturation temperature and enthalpy of ovalbumin at different vanillin concentrations. Using Equation 5.1 , these data can be converted into the dependence of excess denaturation free energy on the vanillin concentrations, Δ d G E ( L ) (Figure 5.4 d). According to the model of ligand binding to identical independent sites, this dependence has a form (Schellman 1975 ):

Δ ΔdE

d bG RT n K L= − × +( )ln 1 (5.10)

where Δ d n is the denaturation increment of the binding site number; K b is the binding constant; L is the free ligand concentration that is approximately equal to the total ligand concentration at an excess of the ligand. This equation fi ts well to the experimental dependence of the excess denaturation free energy of ovalbumin on the vanillin con-centration at the following values of the binding parameters: Δ d n = 20.0 ± 0.1; K b = 5.3 ± 0.1 M − 1 (pH 6.7), and Δ d n = 3.0 ± 0.1; K b = 32.9 ± 1.3 M − 1 (pH 3.0) (Figure 5.4 d). A positive value of the parameter Δ d n means an increase in the number of binding sites caused by the denaturation and also indicates that vanillin binds preferentially to the unfolded D form of the protein. The relative low value of this parameter at pH 3.0 could, however, indicate a noticeable binding of vanillin to the N form of the protein. This may possibly be due to the conformational transition of ovalbumin into a molten globule - like state at the acid pH values (Tatsumi and Hirose 1997 ). The vanillin binding to ovalbumin is nonspecifi c because of the extremely low values of the binding constants. Apparently, it can be liken to solubilization of apolar compounds by surfactant micelles.

In contrast to ovalbumin, bovine serum albumin (BSA) binds the odorants, vanillin, and octanone at pH 6.4 preferentially in the native state (Burova et al. 2003 ). Therefore, the denaturation temperature and enthalpy, and hence the free energy of the protein, increase upon binding of these ligands. Analysis of the experimental dependence of the excess denaturation free energy of BSA on the vanillin or octanone concentration by Equation 5.10 shows that the native form of the protein carries two to three sites of strong binding of these odorants with the binding constants of about 10 3 M − 1 . These estimates agree well with the results of direct determination of the binding parameters using the data of equilibrium dialysis.


Effects of Polysaccharides on Thermal Denaturation of Food Proteins

Biopolymers, polysaccharides in particular, are the most widespread food ingredients that can crucially modify functional properties of proteins and foods. Accordingly, the effects of polysaccharides on functional properties of food proteins were intensively studied (Burova et al. 1992 ; Delben and Stefancich 1998 ; Baeza and Pilosof 2002 ; Burova et al. 2002b ; Zhang et al. 2004 ; Ibanoglu 2005 ; Ibanoglu and Ercelebi 2007 ). A large variety of partially contradictory data were described, however. These contradictory data are due to the fact that the interaction of polysaccharides with food proteins greatly depends on the polysaccharide nature (charged or neutral) and on the solution conditions, such as pH, salt, and polymer concentration. For example, polysaccharides can be either thermodynamically incompatible or able to form soluble and insoluble complexes with proteins (Grinberg and Tolstoguzov 1997 ). We consider effects of polysaccharides on the conformational stability of proteins under conditions of both thermodynamic incompatibility and complexation of proteins with polysaccharides.

The thermal denaturation of 11S globulin from broad beans was investigated in the presence of various anionic (carboxyl - and sulfate - containing) and neutral polysaccharides (Burova et al. 1992 ). It has been shown that 11S globulin forms polyelectrolyte complexes with anionic polysaccharides (both carboxyl - and sulfate - containing) at pH below the protein isoelectric point (pI 4.8). In neutral and weakly basic medium (pH 7.6) at low salt concentration (0.01 M NaCl) 11S globulin is incompatible with the neutral and carboxyl - containing polysaccha-rides. It is however able to form noncooperative complexes with the sulfate - containing polysaccharides. At higher salt concentration (0.4 M NaCl), the complexation is suppressed, and 11S globulin becomes incompatible with the sulfate - containing polysaccharide.

Table 5.2 represents the denaturation temperature of 11S globulin in the presence of polysaccharides at pH 7.6 and pH 4.2. It is notewor-thy that under these conditions the thermograms of 11S globulin alone have a single denaturation peak. In the presence of polysaccharides, two different situations are observed. First, the denaturation peak (peak 1) is close to that of free 11S globulin. Second, there is an additional peak (peak 2) that is shifted to lower temperatures relative to peak 1.


Table 5.2 shows that the polysaccharides incompatible with 11S globu-lin (pH 7.6; 0.01 M NaCl), such as dextran, alginate, pectin, methyl - and carboxymethylcellulose, and Arabic gum, do not affect the thermal denaturation of the protein. Under the same conditions, the sulfate - containing polysaccharides can form complexes with 11S globulin. The result is a destabilized form of 11S globulin coexisting with the free protein. When the complexation is inhibited by 0.4 M NaCl, the peak 2 disappears, and only the peak 1, corresponding to the denatur-ation of the free 11S globulin, remains in the thermogram. These data imply that the 11S globulin - polysaccharide incompatibility does not signifi cantly affect the protein unfolding. On the contrary, the 11S globulin - polysaccharide complexation results in the destabilization of the protein.

At pH 4.2 11S globulin is able to form cooperative electrostatic complexes with anionic polysaccharides, both sulfate - and carboxyl - containing. Under these conditions in the presence of alginate, pectin, and dextran sulfate, the thermograms of 11S globulin have a single denaturation peak (peak 2) at temperatures well below the denaturation temperature of the free 11S globulin (Table 5.2 ). No free protein (peak

Table 5.2. Denaturation temperature of 11 S globulin from broad beans ( ° C) in the presence of polysaccharides. *

Polysaccharide

pH 7.6; q = 20

pH 4.2; q = 1 0.01 M NaCl 0.4 M NaCl

Peak 1 Peak 2 Peak 1 Peak 2 Peak 1 Peak 2

Dextran 74.9 — — — — — Sodium alginate 75.1 — — — — 59.0 Pectin 74.3 — — — — 62.0 Carboxymethylcellulose 75.4 — — — — — Methyl cellulose 74 — — — — — Arabic gum 74.3 — — — — — Dextran sulfate 76.3 63.0 90.3 — — 50.5 ı - Carrageenan 77.9 61.0 90.8 — — — κ - Carrageenan 76.4 64.0 93.1 — — — Free protein 76.0 — 93.0 — 77.0 —

* q is the protein/polysaccharide weight ratio.


1) was observed in studied systems. Consequently, 11S globulin bound to the polysaccharide matrix is substantially destabilized.

The effects of incompatibility and complexation of various polysac-charides on the thermal denaturation and renaturation of a small globu-lar protein from soybean seeds (the Kunitz trypsin inhibitor, KI) were investigated (Burova et al. 2002b ). It was shown that such polysac-charides as dextran, pectin, Arabic gum, dextran sulfate, and ı - and κ - carrageenans do not affect the denaturation parameters of KI under conditions of the protein - polysaccharide incompatibility (pH 8, 0.1 M NaCl). Variation of the protein/polysaccharide weight ratio from 0.01 to 20 did not change this tendency.

The effects of interpolyelectrolyte complex formation of KI with anionic polysaccharides (dextran sulfate, pectin) upon the denaturation of the protein were studied at pH 3.0. Figure 5.5 a shows the denatur-ation thermograms of KI in the presence of dextran sulfate at different values of the protein/polysaccharide weight ratio, q . With increasing content of the protein, the denaturation peak is shifted to the lower temperatures at small q and moves back at higher q .

According to the velocity sedimentation data, the composition of the system was characterized by two components (Figure 5.5 b). The sedimentation coeffi cient of component 1 exceeded that of the free KI (2S). It increased abruptly as the parameter q increased. Obviously, component 1 corresponded to the KI - dextran sulfate complex. The sedimentation coeffi cient of component 2 did not depend signifi cantly on the parameter q and was approximately equal to 3S. It may corre-spond either to “ light ” complexes of approximately constant composi-tion (i.e., having a relatively low content of the bound protein) or to the free dextran sulfate that was characterized by the sedimentation coeffi cient of 2S. It is important that at high q values free KI could not be detected in the system. The result implied that the denaturation transition observed calorimetrically (Figure 5.5 a) could be attributed to the protein bound to the polysaccharide.

The denaturation temperature and enthalpy of KI in complexes depended on the parameter q (Figure 5.5 c). At low protein content, the denaturation temperature of KI was about 15 ° C lower than that of the free protein. When the protein concentration increased, the denatur-ation temperature of KI raised monotonically and at high protein con-centrations reached the value close to the denaturation temperature of free KI. The denaturation enthalpy of KI in complexes with dextran

108


Figure 5.5. Thermal denaturation of soybean trypsin (Kunitz) inhibitor in the pres-ence of dextran sulfate under the protein - polysaccharide complexation conditions (pH 3.0, ionic strength 0.005). (a) Thermograms at the different protein - polysaccharide weight ratios, q . (b) Sedimentation coeffi cients of the components of the protein - polysaccharide mixtures at the different protein - polysaccharide weight ratios; 1: “ heavy ” protein - polysaccharide complex; 2: “ light ” protein - polysaccharide complex. The dashed line corresponds to the free protein (

sw,200 2≅ S). (c) Denaturation

temperature and enthalpy of the protein in the complexes versus the protein - polysaccharide weight ratio. The dashed lines represent the corresponding denatur-ation parameters of the free protein. (d and e) Schematic presentation of the denaturation of a protein ( P ) bound to a polymer matrix ( M ) at the loose (d) and dense (e) protein occupancy. Δ b G N and Δ b G D are free energies of binding of the protein in the native and denatured states to the matrix. Δ T d is the change in the denaturation temperature of the protein due to binding.

sulfate was lower than that of the free protein in the whole range of q values and decreased slightly with increasing q values (Figure 5.5 c).

Let us analyze possible mechanisms responsible for changes in conformational stability of a protein bound to a polymer matrix. The protein stability to denaturation is determined by the free energy of denaturation, Δ d G , which is the difference between free energies of the protein in unfolded, G D , and native, G N , form: Δ d G = G D − G N . When N or D forms of the protein are bound to a matrix, the free energy of each form decreases by a value of the free energy of binding, Δ b G N or Δ b G D , respectively. Reasonably, the free energy of binding depends on the number of contacts formed by the protein upon its fi xation on the matrix. As a rule, the D form of a protein possesses a higher number of accessible binding sites than the N form, consequently Δ b G D << Δ b G N (the case of a preferential binding of the D form). This situation is the most probable in complexes with a low protein occupancy on the matrix (Figure 5.5 d). A gradual saturation of the complex with protein results in a decrease in the number of free binding sites on the polysac-charide matrix (Figure 5.5 e). This leads to a decreasing probability of preferential binding of the D form. In such a densely occupied complex, the stability of bound protein approaches that of the free protein, but does not exceed it.

One could expect that thermodynamic incompatibility of proteins and polysaccharides may result in an increase in the denaturation tem-perature of protein due to the excluded volume effect (Grinberg and Tolstoguzov 1997 ). The minor manifestation of this effect in the case


of 11S globulin and KI could probably signify small changes in the volume of protein molecule upon denaturation. Possibly this is because the thermally denatured proteins adopt a compact conformation of the “ molten - globule ” type or globule clusters. Their molecular volume is apparently not substantially larger than that of the native globule.

Effects of the protein - polysaccharide incompatibility on the protein denaturation could be better pronounced when concentration of the macromolecules is markedly increased (up to 10% and more). The convenient DSC method was applied to a concentrated mixed solution of β - lactoglobulin with κ - and λ - carrageenans, guar gum, xanthan, propylene glycol, and alginate at neutral pH (Baeza and Pilosof 2002 ). In the presence of the polysaccharides, a slight increase in the dena-turation temperature of the protein (of about 2 – 3 ° C) was detected. Similar results were reported for concentrated mixtures of β - lactoglobulin with dextran sulfate and λ - carrageenan (Zhang et al. 2004 ). For these polysaccharides, an increase in the denaturation tem-perature was about 4.6 ° C and 1.2 ° C, respectively. The results imply certainly that under conditions of thermodynamic incompatibility of proteins with polysaccharides, the conformational stability of proteins does not change signifi cantly.

Postdenaturation Aggregation of Food Proteins

Some qualitative features of the postdenaturation protein aggregation are especially pronounced in comparative DSC studies of reversible and irreversible thermal denaturation in concentrated protein solutions (Tsereteli 1982 ; Sochava et al. 1985 ).

When the postdenaturation aggregation is minimal, the protein dena-turation is reversible. In this case, the denaturation temperature and enthalpy as well as the thermogram profi le do not practically depend on the heating rate. On the contrary, the irreversible denaturation is accompanied by signifi cant aggregation of the protein. In this case, the denaturation temperature and enthalpy decrease, and the thermograms are considerably narrowed, with a decrease in the heating rate or an increase in the protein concentration. The aggregation of protein is accompanied by heat evolution and is slower than the protein unfold-ing. A signifi cant difference in the rate of protein unfolding and aggre-gation permits reducing to zero the heat contribution of protein aggregation at suffi ciently high heating rates.


When the rate of protein denaturation is much less than that of the aggregation of the unfolded protein molecules, the aggregation rate is determined by the denaturation rate. In this case, the aggregation process could be considered as a fi rst - order reaction. Under these con-ditions, information about the aggregation kinetics can be directly derived from the DSC denaturation thermograms obtained at different heating rates.

Such an approach was used to study the postdenaturation aggrega-tion of ovalbumin (Weijers et al. 2003 ). From the denaturation tem-perature - heating rate dependence (Sanchez - Ruiz et al. 1988 ) the activation energy and the aggregation frequency factor were deter-mined. As a result, the aggregation rates were calculated for the tem-perature range (67 – 87 ° C), which covers the denaturation heat capacity peak of the protein in the DSC thermogram. The aggregation constant of ovalbumin increases by more than 4 orders of magnitude over the temperature range under investigation.

An attempt was made to extract kinetic parameters of the postdena-turation aggregation of a protein directly from its denaturation thermo-grams (Remmele et al. 2005 ). It was suggested that the unfolded form of protein, U , participates in the aggregation. Its concentration is gener-ally determined by the conformational equilibrium. During the begin-ning stage of the aggregation, dimers of unfolded protein molecules (the form D ) are mainly formed by two paths of dimerization according to the mono - and bimolecular mechanisms. An essence of the model is illustrated by the scheme:

N D

k

k D

Dk

k

U1

24

2

31⎯ →⎯← ⎯⎯ =

⎯ →⎯⎯

⎯ →⎯⎯ ]

(5.11)

where D 1 , D 2 are the fraction of protein molecules aggregating by the mono - and bimolecular mechanisms ( D 1 + D 2 = D ); and k 1 , k 2 , k 3 , k 4 are the rate constants of denaturation, renaturation, mono - and bimo-lecular aggregations, respectively. Temperature dependences of the rate constants are expressed in the spirit of the transition state theory, but taking into account the activation heat capacity increments. An expression for excess heat capacity of protein as a function of tempera-ture and heating rate is derived. This expression in combination with high - performance liquid chromatography (HPLC) data on the aggrega-tion kinetics was used for description of the denaturation thermograms


of a small pharmaceutical protein, interleukin - 1 receptor (type II), at different heating rates. The calculated denaturation temperature and enthalpy coincided with the experimental equilibrium values of these parameters determined in 2 M urea when the aggregation is completely suppressed. The activation parameters of the bimolecular aggregation exceed signifi cantly those of the monomolecular aggregation.

The simplest approach for the application of DSC to study kinetics of the postdenaturation aggregation of proteins is to estimate the appar-ent denaturation enthalpy after heating the protein solution at a given temperature for some time. The protein solution may be considered a mixture of the native and denatured forms. Because in the DSC experi-ment the native protein only gives a heat feedback, a relative content of the native protein can be found from the value of the apparent dena-turation enthalpy, as

Δ Δd N d NH w H wapp ( ) = × (5.12)

where w N is the apparent weight fraction of the native form, and Δ d H is the specifi c denaturation enthalpy of the protein. Hence, it is possible to determine a degree of protein aggregation, w a , for the given preheat-ing time, t (Grinberg et al. 1993 ):

w t w t

H tHa

d

d( ) ≡ − ( ) = −

( )1 1

ΔΔ

app

(5.13)

This approach was used by Wang et al. (2006) to study kinetics of the aggregation of α - lactalbumin at 90 ° C. It was found previously that the amount of the native protein determined by DSC correlates well with that obtained by direct HPLC determination. A kinetic curve of the aggregation w a ( t ) obtained at t = 2 – 25 min could be described by the fi rst - order reaction equation with the rate constant of about 10 − 4 s − 1 . This result seems to signify that unfolding of the protein is a limiting stage of the aggregation.

Conclusion

Thermodynamic analysis of the DSC data on thermal denaturation of food proteins highlighted some key relationships between structure,


interactions, and functional properties of the protein systems. Common tendencies were found in the denaturation mechanism of large oligomeric multisubunit and small globular proteins. Most of them unfold in accordance with the two - state model on the level of a struc-tural domain. Conformational stability of food proteins is fi rst of all affected by pH. Sensitivity of a protein conformation to decrease or increase in pH is mainly defi ned by the number of specifi c side - chain H bonds between ionogenic groups in protein structure. Neutral lyo-tropic salts stabilize native protein conformation in result of two main effects — screening of electrostatic repulsions and lyotropic action of salts on the structure of water. Alcohols decrease the protein confor-mational stability at high temperatures but are able to stabilize proteins at low temperatures. Interpolyelectrolyte complexation of food pro-teins with polysaccharides results in reduced stability of protein native conformation because of the preferential binding of the unfolded protein form with the polysaccharide matrix. Alternatively, under con-ditions of thermodynamic incompatibility of these biopolymers the polysaccharides do not affect signifi cantly the stability of food proteins.

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Chapter 6

Heat - Induced Phase Transformations of Protein Solutions and Fat Droplets in Oil - in - Water Emulsions: A Thermodynamic

and Kinetic Study

Perla Relkin

119

Introduction 119 Heat - Induced Transformations in Protein Solutions 121

Protein Structures 121 Thermodynamics of Protein Heat - Induced Transformations 123 Denaturation - Aggregation of Globular Proteins in Bulk Phase

System 124 Thermodynamics and Kinetics of Heat - Induced Transformations 129

Heat - Induced Transformations in Oil - in - Water Emulsions 132 Crystallization and Melting of Fat Droplets 132 Kinetics of Fat Droplet Crystallization in Oil - in - Water

Emulsions 136 Conclusion 141 References 141

Introduction

The thermomechanical treatments applied for food manufacturing involve batch or continuous heating and cooling steps for mixing, aging, pasteurization, cooking, or storage. Monitoring the effects of heat - induced transformations in raw ingredients and additives can help to optimize such food processing or storage conditions in terms of macro-scopic properties, quality attributes, and shelf life of the fi nal products.


Proteins are built from 20 amino acids constituting polypeptide chains in various spatial arrangements and reactivities that impart structure functionality in food systems. Besides their nutritional role, proteins are used for their ability to form macroscopic structures such as gels or aggregates (Clark and Lee - Tuffnell 1986 ; Donovan and Mulvihill 1987 ; Relkin and Launay 1990 ; Morr and Ha 1993 ; Relkin et al. 1998 ; Mulvihill and Ennis 2003 ; Singh and Hevea 2003 ) or to stabilize emulsions and foams (Walstra 1988 ; Dickinson 1992, 1997 ; Dalgleish 1996 ; Sourdet et al. 2002 ). Particular attention has been paid to heat sensitivity of proteins and consequences for molecular interac-tions in bulk phases in relation to denaturation - aggregation mecha-nisms (De Wit and Klarenbeek 1984 ; Hagolle, Launay, and Relkin 1998 ; Galani and Apenten 1999 ) and on adsorption properties at oil - in - water or gas - in - water interfaces in relation to stability of emulsions and foams (Lef è bvre and Relkin 1996 ; Relkin et al. 1999 ; Dalgleish, Van Mourik, and Corredig 1997 ; Sourdet, Relkin, and Cesar 2003 ). The degree to which the initial conformation state of a protein may be changed is highly dependent on several intrinsic and extrinsic factors. Structural changes that occur during heating vary with time and tem-perature attributes of the process and also with protein characteristics, such as initial conformation state (globular, fi brillar, micellar), concen-tration, and environmental conditions (ionic strength, pH).

Emulsions used for the preparation of whipped cream or ice cream are multicomponent and multiphase systems (Pelan et al. 1997 ; Bolliger, Goff, and Tharp 2000 ). Numerous studies showed that forma-tion of fat crystals from liquid emulsions plays a major role in the stabilization of desired structure - texture and mouth - feel properties of such complex food emulsions (Barfod et al. 1991 ; Walstra and van Beresteyn 1997 ; Boode, Walstra, and de Groot - Mostert 1993 ; Abd El Rahman et al. 1997 ; Bolliger, Goff, and Tharp 2000 ; Relkin and Sourdet 2005 ; Bazmi, Duquenoy, and Relkin 2007 ). In complex systems, fat droplets are stabilized against coalescence - aggregation by using proteins in combination with small - molecular - weight surfactants and with gelatin or polysaccharides. Proteins and surfactants are used for their competitive adsorption properties at the oil - water interface, whereas gelatin or polysaccharides are used for their structuring or thickening properties of the continuous aqueous phase.

Differential scanning calorimetry (DSC), in scanning or isothermal mode, is one of the frequently used techniques to study heat - induced

Heat-Induced Phase Transformations 121

structural changes in food materials (Wright 1984 ; Ruegg, Morr, and Blanc 1987 ; Harwalkar and Ma 1990 ; Roos 1995 ; L ö rinczi 2004 ). It is particularly used for monitoring effects of food composition (nature, concentration, and physicochemical environment of ingredients and additives) on the conformation and structure modifi cations under the time - temperature combinations relevant to processing conditions. Several high - sensitivity microcalorimeters are commercially available (see Chapter 2 ). Although they differ in their characteristics (tempera-ture and heat fl ow detection principles, response time, scanning rate, cell volume), all of them can effi ciently be used to receive signals related to heat - induced transformations in the system being investi-gated. This chapter summarizes some of our previous reviews obtained on heat - induced protein denaturation in model solutions (Relkin and Launay 1990 ; Relkin 1996, 2004 ; Relkin et al. 1998 , 1999 , 2007 ) and presents some new results on fat droplet crystallization in oil - in - water emulsions.

Heat - Induced Transformations in Protein Solutions

Protein Structures

Proteins are of particular concern in a variety of food applications for their structure - forming properties. Their polypeptide chains are more or less tightly packed in different spatial arrangements, depending on the vegetable or animal species from which they are extracted, on the physicochemical environmental parameters used for extraction, and on manufacturing processes, including time - temperature parameters. The amino acid composition of proteins (primary structure) determines their nutritional value, whereas the higher structural organizations of the polypeptide chains (secondary, tertiary, quaternary structures) are related to protein conformational stability, solubility in aqueous medium, and structure - forming properties. The conformation stability of proteins results from a balance of attractive and repulsive forces within the polypeptide chain itself and also between polypeptide amino acids and cosolvent/cosolute molecules or gas or oil - solution inter-faces. Globular proteins, in their “ native ” state, are compact particles with dimensions in the order of magnitude 1 – 10 nm. Their polypeptide chains form secondary structures ( α - helices, β - strands, β - sheets formed between neighboring antiparallel β - strands), high - ordered tertiary


structures (characterized by a hydrophobic core from which H 2 O mol-ecules are squeezed and surface - exposed charged amino acids), and eventually quaternary structures resulting from non - covalent bonding between monomers. Under the effect of heat treatment, the initial structure of globular proteins is altered without hydrolysis of primary covalent bonds. The thermal transition between an initial low - temper-ature state to a high - temperature state is called denaturation (Privalov 1979 ; Brandts and Lin 1990 ; Privalov and Potekin 1996 ). Compared with the protein initial state (native protein), the newly created confor-mational state called denatured is characterized by a lower proportion of high - order structures and a higher exposure to the solvent environ-ment of hydrophobic groups initially buried in the protein core (Mills 1976 ; Cooper 1999 ). In addition, globular proteins possessing disul-fi de, thiol groups prone to SH/S - S interchange, and intermolecular disulfi de bonds have S - S reactions at neutral or basic pH values, espe-cially in denaturing conditions (Liu, Relkin, and Launay 1994 ). Multimeric proteins may dissociate into monomers, before or during denaturation. In such conditions, thermodynamic laws are not relevant to the study of heat - induced transformations, which may occur succes-sively or simultaneously with interaction mechanisms between unfolded proteins themselves or between other solute or surfaces, contributing to aggregation or gelation, ligand binding, and interfacial properties (Lef è bvre and Relkin 1996 ).

Gelatin molecules derive from chemical transformation of collagen. They display extended overall shapes, but likely as for the other poly-peptides they present α and β secondary structures sharing basically similar conformational change properties under heating and cooling. But contrary to globular proteins, and likely for linear polysaccharide chains, gelatin molecules undergo heat - induced reversible - ordered helix - to - disordered random coil transitions in most of the conditions used in a variety of food applications (low - fat yogurt or creams, mousses). Due to their thickening and gelling properties, they are added to milk proteins to improve the texture and fi rmness of dairy products (Fiszman, Lluch, and Salvador 1999 ).

Caseins, the major milk protein component, is not susceptible to heat - induced denaturation. When considered as individual molecules, they are much less compact and organized than other proteins, such as gelatin (helical secondary structure), or globular proteins (high - order tertiary structure). However, the degree to which globular proteins


(e.g., whey, blood plasma, egg white, soya proteins) or extended pro-teins (gelatin) can interact with casein is also related to their structural properties (Haque, Kristjansson, and Kinsella 1987 ; Kinsella and Whitehead 1989 ).

Thermodynamics of Protein Heat - Induced Transformations

The denaturation mechanism of small - molecular - weight globular pro-teins may occur after a reversible two - state model (Privalov 1979 ; Brandts and Lin 1990 ; Relkin 1996 ; Cooper 1999 ):

NK

native U denatured unfoldedeq

( )⇔ ( ) (6.1)

K T U

Neq ( ) = [ ][ ]

(6.2)

Δ Δ ΔG T H T T S T K TNU

NU

NU( ) = ( ) − ( ) = − ( )RT ln (6.3)

where, ΔG TNU ( ), ΔH TN

U ( ), and ΔS TNU ( ) are the variations of Gibbs free

energy, of enthalpy, and of entropy, respectively, upon unfolding. When globular proteins are exposed to denaturing conditions, the

equilibrium constant K eq ( T ) is shifted to favor the unfolded state. At T = T max , the temperature at which approximately half of the initial amount of the proteins have altered structures, the equilibrium constant K eq ∼ 1 (Equation 6.2 ) and the free energy change under denaturation Δ G ( T max ) ∼ 0 (Equation 6.3 ). T max depends on several parameters, including protein initial state and concentration, pH, ionic strength, and presence of cosolvent.

In practice, all protein preparations used in food applications are mixtures of several protein species, and they may contain other solutes (salt, sugar, traces of polysaccharides) that have effects on the protein ’ s thermal behavior. In this case, protein heat - induced denaturation in food systems does not take place after the reversible two - state model, and other consecutive or successive reactions may be triggered by unfolding or compete with protein refolding (Lef è bvre and Relkin 1996 ).

In DSC methodology, the reversibility of the denaturation process is typically monitored by a second heating scan of the sample. If the second heating thermogram does not show a peak, then the thermal reaction may proceed according to the scheme in Equation 6.4 :


N xD xD xDx

K k knative denatured unfolded st

1 2 i

eq 1 2

( ) ⇔ ⇒ ⇒ aate aggregated

( )⇒ ( )A

(6.4)

If k i ≥ K eq , most of denatured proteins are converted irreversibly into A species (aggregates) and the thermal behavior of the system is kineti-cally controlled by the slowest conversion reaction.

Denaturation - Aggregation of Globular Proteins in Bulk Phase System

In food - manufacturing conditions, the reversibility of the process is hindered by high protein concentrations and added salts that may increase protein - protein interactions. Other chemical reactions, such as deamination of amino acid residues, hydrolysis of peptide bonds, dis-ruption of disulphide bonds, and isomerization of proline residues, may also hinder the refolding of the polypeptide chain into the native folded conformation for stereoisomeric reasons (Kinsella and Whitehead 1989 ). The lack of protein refolding may be related to the loss of solu-bility and to modifi cation of protein functionality in food products (Relkin 1996 ). Over a temperature range between 60 ° C and 80 ° C, protein denaturation is caused by weakening of hydrophilic interac-tions (hydrogen bonds, van der Waals interactions, electrostatic inter-actions between charged groups, specifi c binding) and by strengthening of hydrophobic interactions. The hydrophobic interactions are exother-mic whereas the breaking of the other bonds is endothermic (Relkin and Launay 1990 ).

Identifi cation and evaluation of parameters related to conformation stability and functionality of food components are of great importance in monitoring effects of manufacturing parameters and particularly in optimizing food processing and storage conditions for improving quality of food products. DSC, a noninvasive technique, is particularly interesting for monitoring protein conformational changes from the native initial state to another one through the change of one thermo-dynamic parameter: the temperature (Brandts and Lin 1990 , Privalov and Potekin 1996 ; Lef è vre and Relkin 1996 ). Commercially available calorimeters working on the basis of different measuring principles (power compensation or heat fl ux calorimeters) determines the heat fl ow difference between a sample and reference containers during the


heat - induced reactions occurring in the sample material. Calorimetric parameters associated with a protein thermal transition from the initial conformation state to another one are extracted from the DSC signal obtained from the protein solution after subtraction of the baseline DSC signal. This signal, corresponding to the equipment baseline, is obtained by using two pans fi lled with reference materials (buffer for study of transitions in protein solutions). The transition temperature mostly used for evaluation of protein denaturation is that of peak maximum ( T max ) temperature of maximum deviation of the heat fl ow signal. For a solution containing one protein, T max corresponds to tem-perature of the maximum rate of the protein reaction, and it is close to ∼ 50% denaturation reaction. For a mixture of proteins in solution, T max corresponds to the reaction of the major component, and it can be preceded or followed by shoulders due to the presence of less or more conformationally stable proteins.

A DSC study of protein heat - induced transformations was performed from solutions of a whey protein isolate that was obtained by ultrafi l-tration of skimmed milk. We used highly sensitive DSC equipment (micro - DSC III; SETARAM, Caluire, France), working with ∼ 800 μ L volume of samples and scanning rates ranging from 0.1 ° C.min − 1 to 1.2 ° C.min − 1 , from – 20 ° C to 120 ° C. The thermograms in Figure 6.1 were obtained at a low heating rate (0.1 ° C.min − 1 ) using a whey protein isolate that was dispersed in distilled water at protein concentrations ranging from 2% and 10% at pH 6.6.

The maximum deviation of heat fl ow corresponds to the denatur-ation of β - lactoglobulin (major whey protein component) and the shoulder located at T < T max corresponds to the denaturation of α - lactalbumin (25% protein content). The apparent heat of reaction, Q cal, required for the thermal transition is determined from the area between the peak and a sample baseline drawn from temperatures correspond-ing to pre - and post - transitions (maximum amount of proteins in the initial and fi nal states, respectively) divided by the amount of reacting materials in the sample pan. Heat - induced transformations in protein solutions used in food manufacturing occur without a signifi cant shift between the pre - and post - transition region, and approximation of a straight baseline drawn by interpolation between the beginning and the end points of the transition is usually used.

Peak temperature and total enthalpy change of protein solutions depend on several factors. Changes in heating rate, protein concentra-


tion, or other extrinsic factors (pH, added salts or other cosolutes) could be applied to resolve superimposed phenomena (Relkin 1996 ). For example, an exothermic reaction (aggregation) may be superim-posed with an endothermic reaction (dissociation of polymeric proteins to monomers, unfolding process) during the DSC run, as described below. DSC curves obtained at heating rates ranging from 1 ° C to 0.1 ° C.min − 1 for a solution containing 4.15% protein, 1.2% ash, and 2.5% lactose at pH 6.6 are shown in Figure 6.2 .

The shape of the DSC signals reveals one single endothermic peak for d T /d t < 0.5 ° C.min − 1 . For higher scan rates, the DSC signals present a major endothermic peak and a slight exothermic reaction event at a temperature lower than T max , the temperature of maximum deviation of the endothermic signal. Due to different heat transfer properties, depending on the heating rate, heat - induced protein transformations within the sample volume seem to behave differently. The DSC curves shown in Figure 6.1 were registered from protein solutions at higher concentrations and a low heating rate (0.1 ° C.min − 1 ), at which thermal equilibrium within the sample volume could be expected. In these experimental conditions, heat - induced transformations were appar-

2%

4%

6%

20

Endoth

erm

ic h

eat flow

, W

·g–1

30 40 50 60 70 80

10%

Figure 6.1. Heating curves obtained from solutions of whey proteins at different concentrations (pH 6.6, 0.1 ° C.min − 1 ).


ently refl ected by one single endothermic signal, and calorimetric parameters ( T max and Q D ) of apparent heat of reaction obtained from solutions at protein concentrations ranging from 2% to 25% are reported in Figure 6.3 .

As suggested in previous studies (Lef è bvre and Relkin 1996 ), the decrease in the apparent heat of reaction and the increase in the peak temperature values with increasing protein concentration may be assumed to be due to enhancement of hydrophobic interactions, as a result of simultaneous unfolding (endothermic) reactions of proteins and protein - protein interactions (exothermic). Considering the simpli-fi ed scheme represented by Equation 6.4 , if k > K eq , most of denatured proteins can be converted irreversibly into aggregates and the thermal behavior of the system is kinetically controlled by the rate - limiting denaturation step reaction. Following this mechanism, the decrease in Q D (apparent heat of reaction) and increase in T max (major peak tem-perature) observed from solutions containing increased dry matter compositions (of which 2% to 25% protein) could be explained by increasing values of the equilibrium constant ( K eq ) and increased con-

20 mW·g–1

endo

0.75

0.5

0.25

0.1

1

Endoth

erm

ic h

eat flow

40 50 60 70

Temperature, °C

80 90

Figure 6.2. Heating curves obtained at different scanning rates from a solution of whey proteins at 4.15% protein concentration (pH 6.6).


centration of denatured states (shift to the right of the equilibrium reaction). Similar trends were observed for globular protein solutions at a pH close to the protein isoelectric pH or in the presence of added salts for which the net protein surface charge is either close to zero or screened by electrolyte opposite charges, respectively (Relkin and Launay 1990 ; Relkin 1996 ; Fiszman, Lluch, and Salvador 1999 ). In both of these cases, peak temperature could increase and Q D decreases.

Aggregation and denaturation mechanisms involving protein - protein interactions (exothermic reaction) and breaking down of inter-nal low - energy forces between amino acids (endothermic reaction) can be superimposed in the same temperature range. This may explain the overall trends in the calorimetric energy, Q cal , and temperatures, T max , as due to denaturation - aggregation changes during the DSC runs.

Thus, among the heat - induced reactions occurring in globular pro-tein solutions, aggregation and denaturation mechanisms may be overlapped, leading to either one single endothermic curve at scan rate < 0.5 ° C.min − 1 for protein concentrations up to 25%, or to successive endothermic and exothermic reactions, which became distinguishable at higher heating rates. At neutral pH, whey proteins are negatively charged, and increasing the protein and salt concentrations may favor

Peak tem

pera

ture

, °C

Appare

nt h

eat o

f denatu

ratio

n, J

·g–

1

74

72

24

22

20

18

16

14

12

10

70

68

66

30252015

% Protein concentration

1050

Figure 6.3. Variations of peak temperatures ( T max in ° C) and heat of reaction ( Q cal in J.g − 1 ) obtained from solutions of whey proteins at the indicated concentrations (pH 6.6, 0.1 ° C.min − 1 ).


interaction properties between proteins as they denature during the DSC run. Thus, dissociation of noncovalently bound protein aggregates and their unfolding mechanism followed by irreversible aggregation could explain the increase in peak temperature in parallel with the decrease in the apparent heat of reaction with increasing protein concentration.

Thermodynamics and Kinetics of Heat - Induced Transformations

If the heat involved in the aggregation step D ⇒ A is much lower compared to that of the fi rst denaturation step N ⇔ D , then the calo-rimetric heat of reaction could be very close to that of the enthalpy change value of denaturation. In an earlier study, the activation energy of protein heat - induced denaturation was determined by using peak temperature values obtained from thermograms registered at different scan rates or by using partial completion of DSC reaction as a function of temperature (Relkin and Launay 1990 ). The activation energy (kJ.mol − 1 ) of heat - induced denaturation of whey protein varied with protein concentration (400 < E A < 550 kJ.mol − 1 for protein concentrations ranging between 3.5% and 24%). Considering the effects of protein concentration on T max , Q cal , and E A , it was suggested that the reaction mechanism of denaturation may involve a fast initial step of partial dissociation - unfolding, followed by a slow interchain hydrophobic reaction. In another study (Sanchez - Ruiz 1992 ), the recording of ther-mograms at different scan rates ( β ) and corresponding T max values were applied to the Lumry - Eyring model (Lumry and Eyring 1954 ) for evaluation of the activation energy ( E A ) and pre - exponential factor ( Z ) using the following relation:

ln ln

max max

βT

ZRE

ERTA

A⎛⎝

⎞⎠ = ⎛

⎝⎞⎠ −

(6.5)

Activation enthalpy ( Δ H # ) and entropy ( Δ S # ) were deduced from the following relations:

ΔH E RTA# = − (6.6)

ΔSR

Zh ek Tb#

ln ln= ( ) − ( )

(6.7)


where, k B is the Boltzmann constant (1.38 10 − 23 J.K − 1 ), T temperature in K, and h is the Planck constant (6.62 10 − 34 J.s). Plots of ln ( β / T max ) = f (1000/ T max ) shown in Figure 6.4 were obtained by applica-tion of Equation 6.5 to T max recorded from a protein solution (5.15%; pH 6.6) using a small volume sample (45 μ L) and scanning rates ranging from 15 ° C.min − 1 to 2.5 ° C.min − 1 (curve a), or using a large volume sample (750 μ L) and scanning rates ranging from 1 ° C.min − 1 to 0.1 ° C.min − 1 (curve b). By using the same protein solution but two different calorimeters (classical DSC working at high scan rates and small volume pans or highly - sensitive DSC working at low scan rates and large - volume vessels), E A (activation energy) values (Table 6.1 ) deduced from the slope of the linear parts of these two representations were very similar. However, the differences between the calorimetric parameter, Q cal (determined from the surface area under the transition peak), and the thermodynamic parameter, Δ H # (deduced from Equations 6.5 and 6.6 ), were very much lower when evaluated from DSC mea-surements at scanning rates ranging between 0.1 ≤ β ≤ 1 ° C.min − 1 than between 2.5 ≤ β ≤ 15 ° C.min − 1 . Following the Lumry - Eyring theory,

ln (

β/T

max)

2.86 2.88 2.9

1000/Tmax

2.92 2.94

–3

–4

–5

–6

–7

–8

Figure 6.4. Lumry - Eyring representation obtained from protein solutions (4.15%, pH 6.6) at two different ranges of scanning rates: classical DSC working in a high scan rate range (empty circles), and highly sensitive DSC working in a low scan rate range (fi lled circles). See text.


Table 6.1. Examples of calorimetric ( T max , Q cal ) and thermodynamic ( E A , Δ H # ) parameters, deduced from DSC heating curves obtained from a protein solution (4.15% concentration, pH 6.6) using two weight samples and two ranges of scanning rates. Lumry - Eyring theory was applied to DSC curves obtained at the indicated scan rates, and activation energy values, E A , were calculated from the linear part of plots in Fig. 5.4 (see text).

Sample weight (mg)

d t /d T ( ° C.min − 1 )

T max ( ° C)

Q cal (kJ.mol − 11 )

E A (kJ.mol − 11 )

Δ H # (kJ.mol − 11 )

750 0.1 68.2 283 342 340 45 5 76.3 232 345 342

these differences could indicate differences in heat - induced conforma-tion transitions and reaction mechanisms, depending on the heating rate and constant rate of reactions, as suggested previously (Relkin and Launay 1990 ; Sanchez - Ruiz 1992 ; Relkin 2004 ).

Milk proteins are composed by approximately 80% caseins in micelle form and 20% whey proteins, of which half are β - lactoglobulin. The DSC curves (1 ° C.min − 1 ) in Figure 6.5 were obtained from solu-tions in simulated milk ultrafi ltrate (SMUF, pH 6.6) of a whey protein isolate, alone or in mixture with 20% casein or 40% casein at 5.3% total protein concentration. These curves showed the presence of an exothermic reaction occurring at T > 80 ° C. Partial replacement of whey proteins by 20% or 40% casein micelles gave DSC curves composed of a major endothermic peak at T max , accompanied by an exothermic effect at T > T max .

The intensity of the exothermic event seems to increase, whereas T max seems to decrease with increasing casein - to - whey protein weight ratio. The lowering of T max with increased proportion of casein to whey proteins could be explained by an increase in the rate of irreversible aggregation mechanism between casein and unfolded whey proteins. Upon heating, some of the hydrophilic interactions (hydrogen bonds, van der Waals interactions, electrostatic interactions between charged groups, specifi c binding) are weakened, whereas some of the hydro-phobic amino acids (initially buried in the interior core of whey pro-teins) become more exposed at the surface. In the example of Figure 6.1 (2% protein in water; β > 0.5 ° C.min − 1 ), the exothermic signal is shown at T < T max , whereas in the example of Figure 6.5 (5.3% protein


in SMUF, β = 1 ° C.min − 1 ) it is seen at T > T max . This difference may be due to the presence of lactose, which increases the protein resistance to heat - induced denaturation (Park and Lund 1984 ; Ruegg, Morr, and Blanc 1987 ).

Heat - Induced Transformations in Oil - in - Water Emulsions

Crystallization and Melting of Fat Droplets

Oil - in - water emulsions are constituted by a dispersing aqueous medium, oil - water interface, and dispersed fat droplets (Dickinson 1992 ; Dalgleish 1996 ; Walstra 1998 ). Globular proteins, due to their amphi-philic (polar/nonpolar) nature and their marginal conformational stabil-ity, may adsorb from aqueous solutions to solid surfaces and fl uid - fl uid interfaces. They act as surfactants by reducing the interfacial tension and forming a cohesive fi lm (Walstra 1988 ; Dickinson 1997 ; Sourdet et al. 2002 ). Denatured proteins, compared with “ native ” proteins, which are characterized by a less - ordered structure related to higher fl exibility and surface hydrophobic index (due to a greater exposure to the aqueous medium of initially buried hydrophobic), were shown to accommodate easier to oil - solution interfaces.

Monitoring heat - induced transformations of fat droplets in oil - in - water emulsions as a function of their composition is of great techno-logical interest in relevance to their physical stability against coalescence (Walstra and van Beresteyn 1975 ; Boode Walstra and de Groot - Mostert 1993 ; Relkin and Sourdet 2005 ). In the formulation of many oil - in -

Heat flow

(m

W)

3.5

3

2.5

2

1.5

(c)

(b)

(a)

140 60

Temperature (°C)

80

Figure 6.5. Heating curves obtained from solutions of whey proteins, alone (a), or in mixtures with either 20% casein (b), or 40% casein (c). 5.3% total protein, pH 6.6 in simulated milk ultrafi ltrate, 1 ° C.min − 1 .


water food emulsions, proteins are used in combination with small - molecular - weight emulsifi ers (surfactants) and polysaccharides (Dickinson 1998 ). Proteins compete with surfactant molecules for the adsorption to the oil - water interface, giving appropriate interfacial properties, whereas polysaccharides are used as thickeners of the aqueous continuous phase. In addition to these parameters that have effects on colliding properties of fat droplets and their resistance to coalescence, crystallization behavior of fat droplets was shown to play a major role in stability and instability of food emulsions. Several techniques may be used to study thermal behavior of fat in bulk and emulsifi ed phases (Dickinson and McClements 1995 ; Hindle, Povey, and Smith 2000 ; Garti and Sato 2001 ). Because crystallization of fat releases a large amount of heat, numerous studies were performed using DSC in nonisothermal and isothermal modes. It was shown that crystallization temperature of dispersed fat droplets is lowered, com-pared to bulk fat (Skoda and van den Temple 1963 ; Walstra and van Beresteyn 1975 ; Walstra, Kloeck, Vliet Ton van 2001 ). In addition to droplet curvature, supercooling needed to initiate crystallization of fat globules depends on several other factors, including the origin and composition of fat, adsorbed materials, and, particularly, added lipo-philic or hydrophilic emulsifi ers (Dickinson and McClements 1995 ; Garti and Sato 2001 ; Relkin and Sourdet 2005 ; Relkin et al. 2008 ). Besides oil - in - water food emulsions, such as sauce or mayonnaise where polyunsaturated lipids are used, there are other types of emul-sions prepared from anhydrous milk fat (AMF) that are used for fab-rication of dairy whipped cream or ice creams. AMF is constituted by a wide diversity of saturated and unsaturated triacylglycerols (TG), each characterized by its own melting temperature (Hartel and Kaylegian 2001 ). The physical properties of AMF, resulting mainly from its extraction processing and TG composition, have different temperature dependency. AMF has broad melting and crystallization temperature ranges from approximately − 40 ° C to 45 ° C, and it may contain more than 50% crystalline fat when stored at 5 ° C (refrigerator temperature). Therefore, the manufacturing process of dairy emulsions consists of successive steps. In the fi rst step, AMF is heated above its melting temperature (50 ° C), and lipophilic emulsifi ers are dispersed in the lipid melt. In the second step, this lipid - melt phase is mixed by stirring with the aqueous phase, which contains water - soluble ingredi-ents (proteins and polysaccharides). In the third step, the premix is


passed through a high - pressure homogenizer, and the resulting emul-sion is cooled down to a storage or ageing temperature (Relkin, Sourdet, and Fosseux 2003 ). Emulsions used to prepare dairy whipped cream or ice creams are aged at 4 ° C for a time ranging from 10 to 18 h. During the aging step, in addition to complete hydration of polysac-charides and partitioning properties of surfactants and proteins between the oil - in - water interface and the aqueous continuous phase, the behav-ior of fat droplets and their heat - induced transformations are consid-ered as key factors for the development of desired structures in the fi nal product (Abd El Rahman et al. 1997 ; Goff 2002 ).

DSC is used to study thermal behavior of ingredients (fat, surfactant, polysaccharide) as a function of processing parameters, and especially for evaluation of supercooling and kinetics of fat crystallization from a liquid emulsifi ed phase. Supercooling, needed to initiate fat crystal-lization from melted systems can be evaluated from the cooling and re - heating DSC signals registered in a scanning mode. Examples of DSC curves obtained from AMF in bulk phase ( ∼ 20 mg) or fat droplets in protein - stabilized emulsions ( ∼ 80 mg) are shown in Figures 6.6 and 6.7 . All the samples were heated to 50 ° C (crystal melting) before cooling. The curves in Figures 6.6 and 6.7 were obtained from cooling and reheating cycles at 0.5 ° C.min − 1 . In the Figure 6.7 , besides the DSC cooling (a) and reheating (b) curves (0.5 ° C.min − 1 ), we present also the melting curve (c) obtained after cooling to 4 ° C and holding the emul-sion at this temperature for 10 h.

These curves present distinguishable exothermic and endothermic events corresponding to crystal formation and melting of crystals (or polymorphs), respectively. Comparison of the temperatures of the initial scan and scans after cooling and reheating indicated a higher supercooling in emulsifi ed samples than in bulk fat samples (Table 6.2 ). The shape of the melting curves obtained by reheating (0.5 ° C.min − 1 ) just after cooling at the same scan rate was very similar for all the fat samples (Figures 6.6 and 6.7 ). However, the shape of the crys-tallization curves differed depending on the fat sample (bulk - or emul-sifi ed - fat sample) and ingredient composition. Melting curves (0.5 ° C.min − 1 ) obtained after a holding step at 4 ° C for 10 h applied to bulk (AMF - 0 and AMF - S) or emulsifi ed (E - 0 and E - S) fat samples in the absence or presence of surfactant, respectively, show a broad endo-thermic curve (Figure 6.7 , curve c), with T max (maximum peak tem-perature) located at around 20 ° C and Q cal (apparent heat of reaction)

endo

AMF

AMF-SHeat flow

, m

W

Temperature, °C

30 4020100

Figure 6.6. Cooling and heating curves (0.5 ° C.min − 1 ) obtained from anhydrous milk fat alone (AMF) or with 1.75 wt% added surfactant (AMF - S).

Heat flow

, m

W·g

–1

Temperature, °C

30 4020100

endo

(c)

(b)

(a)

Figure 6.7. Cooling (a) and fi rst reheating (b) curves, and second reheating curve (c) observed at 0.5 ° C.min − 1 from protein - stabilized AMF emulsion containing added surfactant (E - S). The second reheating curve (c) was registered after 10 h holding of the emulsion at 4 ° C.

135


Table 6.2. Calorimetric parameters observed from cooling and heating thermograms (0.5 ° C.min − 1 ) obtained from anhydrous milk fat sample in the absence of added surfactant (AMF - 0) or in presence of 1.75% surfactant (AMF - S), from protein - stabilized emulsion in the absence of surfactant (E - 0), and in protein - stabilized emulsion in the presence of surfactant E - S. T max values correspond to temperature of peak maximum observed in the melting curve of emulsions, which were held at 4 ° C for 10 h (see text).

Sample T cris ( ° C)

T end ( ° C)

T max (10 h) ( ° C)

AMF 22.4 38.0 20.6 AMF - S 19.7 38.0 20.6 E - 0 19.9 38.0 21.1 E - S 19.1 35.5 20.2

close to 65 J.g − 1 . This indicates that for both bulk and emulsifi ed fat samples there was formation of a similar amount of crystalline fat during the 10 - h aging at 4 ° C.

Application of DSC in isothermal mode (4 ° C) to the same bulk fat and emulsions led to observation of a single exothermic heat fl ow signal as seen in Figures 6.8 and 6.9 . The maximum heat fl ow devia-tion of this exothermic peak occurs after different holding times at 4 ° C. Compared with the bulk AMF - 0 sample, this event seemed to occur after a similar holding period (27 min) in E - S (emulsion with surfactant). However, it seems to be anticipated for AMF - S (bulk fat in presence of surfactant) and more delayed ( ∼ 5 min) in the protein - stabilized emulsion without added surfactant.

Kinetics of Fat Droplet Crystallization in Oil - in - Water Emulsions

Analysis of the heat fl ow pattern involved during the isothermal step could be used for evaluation of crystal growth characteristics, such as the induction time and growth rate values. From the determination of the partial apparent heat of reaction at time t (calculated from the partial area under the exothermic heat fl ow), it is possible to obtain the fractional fat crystallization, X ( t ), from the following relation:

X t A t

Q( ) = ( )

cal (6.8)

Holding time at 4°C, min

60 8040100

Endoth

erm

ic h

eat flow

, m

W·g

–1

AMF-0

AMF-S

Figure 6.8. Isothermal curves registered at 4 ° C from anhydrous milk fat alone (AMF - 0) or with 1.75 wt% added surfactant (AMF - S).

Endoth

erm

ic h

eat flow

, m

W·g

–1

E-0

2

E-S

Holding time at 4°C, min

60 8040200

Figure 6.9. Isothermal curves registered at 4 ° C from protein - stabilized emulsion, without added surfactant (E - 0) or with added surfactant (E - S).

137


where Q cal is the total calorimetric heat of reaction calculated from the area under the exothermic peak registered during the holding time.

The mechanism of fat crystal growth can be described by the Avrami equation (Avrami 1939 ):

X t t n( ) = − − ( )⎡⎣ ⎤⎦1 exp α (6.9)

or can be linearized:

ln ln ln ln− − ( )( )[ ] = ( ) + ( )1 X t n tα (6.10)

In this expression, α represents the nucleation rate of homogeneous crystallization and n (Avrami index) represents the rate of crystal growth, with n ∼ 3 for a disklike crystal growth mechanism and n ∼ 4 for a spherulic crystal growth mechanism (Tore - Vazquez et al. 2002 ). Avrami plots obtained by applying Equation 6.10 to DSC data obtained for AMF - 0, E - 0, and E - S fat samples under isothermal condition at 4 ° C are shown in Figure 6.10 . They present a linear variation in a short time region, with different slope values (2.7 < n ≤ 4), suggesting dif-ferent crystal growth mechanisms. Application of the Avrami model to fat crystallization is valid for a homogeneous mechanism, whereas noninteger values might suggest heterogeneous and secondary nucle-ation. Results in Table 6.3 could suggest a spherulic crystal growth mechanism ( n ∼ 4) in fat droplets in the protein - stabilized emulsion, without added surfactant.

The growth of fat crystals with a sigmoidal time variation may also be modeled using the modifi ed Gompertz equation, as follows (Walstra, Kloeck, Vliet Ton van 2001 ):

X t X e

Xt t( ) = ∗ − −( ) +⎡

⎣⎢⎤⎦⎥{ }max

max

maxexp exp μ

ind 1

(6.11)

In this model, X max is the asymptotic value of fractional completion of crystallization, μ max is the slope at the time when the growth of crystals becomes exponential (steepest ascent of the sigmoid curve), and t ind is the induction time (intersecting this line with the t - axis). μ max and t ind are adjustable parameters determined from partial integration of the heat fl ow signal registered by the DSC isothermal method.


1.5 2 2.5 3

ln (t), min

3.5 4 4.5

4

2

0

–2

–4

–6

–8

ln (

–ln

(1-T

S))

Figure 6.10. Avrami plots obtained from anhydrous milk fat alone (square symbols), from protein - stabilized emulsion without added surfactant (diamonds), or with added surfactant (circles).

Table 6.3. Kinetic parameters of fat crystallization at 4 ° C in anhydrous milk fat sample (AMF - 0), in protein - stabilized emulsion (E - 0), without addition of surfactant, and in protein - stabilized emulsion with added surfactant (E - S). The kinetic parameters were deduced by application of Gompertz and Avrami models (see text).

Gompertz model Avrami index

t max min

t induction min

μ max min − 1 n R 2

AMF - 0 27.2 ± 2.1 14.2 ± 0.12 3.48 3.461 0.999 E - 0 32.7 ± 1.2 22.4 ± 0.04 4.00 4.038 0.992 E - S 27.0 ± 3.0 13.5 ± 0.02 3.28 2.654 0.999

The experimental curve obtained from partial integration of the heat fl ow signal as a function of holding time and the sigmoid curve, obtained by applying Equation 6.11 to X ( t ), are compared in Figure 6.11 . Values of t max (maximum deviation of the exothermic DSC signal), n (Avrami index), and Gompertz parameters (values of induction time, t ind , and maximum growth rate, μ max ) are reported in


Table 6.3 . According to the Gompertz method, AMF in the bulk phase and in the emulsion containing milk proteins with surfactant (E473) have very close t max (27 min) and t ind values (14 min), an n value of 3.5 and 2.7, respectively, and an μ max value of 3.5 min − 1 and 3.3 min − 1 , respectively. On the other hand, the protein - stabilized emulsion (E - 0) without added surfactant exhibits a higher value of n ∼ 4 (indication of a spherulic crystal growth) and higher values of t max , t ind (delay in nucleation), and μ max (increase in the crystal growth rate).

Emulsifi cation procedure and ingredient complexity have a domi-nant role in characteristics of fat droplets, such as particle average diameters and size distributions, composition and physical properties of surrounding surface layers, and crystalline fat content and polymor-phism (Skoda and van den Tempel 1963 ; Walstra 1975 ; McClements et al. 1993 ; Dickinson and McClements 1995 ; Kaneko et al. 1999 ; Hindle, Povey, and Smith 2000 ; Relkin, Sourdet, and Fosseux 2003 ; Relkin and Sourdet 2005 ). The supercooling effect (temperature needed to initiate fat crystallization in globules) has been shown to differ depending on fat composition and mean droplet size of fat droplets and on the concentration and lipophilic or hydrophilic nature of emulsifi ers

50

Endoth

erm

ic h

eat flow

, m

W·g

–1

Holding time at 4°C min–1

60 80 10040200

120

100

80

60

40

20

0

X (t)

Figure 6.11 Examples of time evolution of X ( t ) and partial completion of crystalliza-tion reaction at 4 ° C, determined from anhydrous milk fat sample (experimental values, circles) and its Gompertz representation (sigmoidal dashed curve), as deduced from the endothermic heat fl ow signal (see text).


(Garti and Jano 2001 ). D 50 values (average median diameters of fat droplets) determined from laser light scattering measurements (Relkin and Sourdet 2005 ) were close to 3.1 μ m for E - 0 and E - S emulsions. However, whereas a similar supercooling was determined by DSC in scanning mode (Table 6.2 ), kinetic parameters ( t max , t ind , μ max ) and n (Avrami index) deduced from the DSC isothermal method were dif-ferent (Table 6.3 ). These results indicate, as expected from numerous studies, that hydrophobic surfactant acts as a catalyzer for crystal nucleation in fat globules, where crystallization mechanism is consid-ered as homogeneous.

Conclusion

DSC has been used for several years to investigate heat - induced con-formational or structural changes of a broad range of food ingredients (biopolymers, proteins, fats, sugars, emulsifi ers) in various physico-chemical conditions. Most of the previous DSC studies showed the ability of food ingredients in heat - induced structural or physical state changes, which are of great importance for manufacturing of food products with controlled structures. The examples described in this chapter indicate that combining DSC in nonisothermal and isothermal methods can provide thermodynamic and kinetic data to contribute to better understanding and control of structure - forming mechanisms in food systems.

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Chapter 7

Analysis of Foodborne Bacteria by Differential Scanning Calorimetry

Michael H. Tunick , John S. Novak , Darrell O. Bayles , Jaesung Lee , and G ö n ü l Kaletun ç

147

Introduction 147 C. perfringens and L. monocytogenes Analysis by DSC 149

Sample Preparations 149 C. perfringens Results 150 L. monocytogenes Results 152

Effect of Antibiotics on Bacteria 153 E. coli and Lactobacillus plantarum Analysis by DSC 155

Sample Preparations 156 E. coli and L. plantarum Results 156

Application of DSC for Evaluation of Food - Processing Treatments 158 Determination of Heat Inactivation Parameters of Bacteria from

Calorimetric Data 158 Determination of Effi cacy of Nonthermal Treatments from

Calorimetric Data 161 Determination of Impact of Antimicrobials on Bacteria from

Calorimetric Data 163 Conclusions 164 References 164

Introduction

The World Health Organization estimates that 325,000 hospitalizations and 5000 deaths result from foodborne illness in the United States each


year (WHO 2007 ). Tens of thousands of cases of foodborne illness in the United States each year are the result of contamination by Clostridium perfringens (Mead et al. 1999 ), a spore - forming anaerobe that may initiate spore production in response to acidic conditions in the gastrointestinal tract (Novak, Tunick, and Juneja 2001 ). Illness due to Listeria monocytogenes is much less prevalent but far more serious, leading to 500 deaths in the United States annually (Mead et al. 1999 ). These and other foodborne pathogens can be inactivated by heat or antibiotics, which alter the effi cacy of protein synthesis in ribosomes. Ribosomes, which are organelles found in the cytoplasm of all cells, assemble amino acids into proteins by using the directions supplied by messenger RNA molecules (Borman 2007 ). In bacteria, ribosomes consist of a small 30S subunit and a large 50S subunit about twice the size of the smaller subunit, which fi t together to form the 70S ribo-some. An Escherichia coli cell contains thousands of ribosomes, each made up of three RNA components and over 50 proteins weighing 2.5 × 10 6 Da (Borman 2007 ). Stressing microorganisms at relatively high or low temperatures, known as heat shocking or cold shocking , decreases their thermal tolerance by impairing the 30S subunit (Stephens and Jones 1993 ). This decreased thermal tolerance can be measured by determining the microorganism ’ s D 60 value, which is the length of time required for the viable population to decrease 10 - fold at 60 ° C.

About 35% – 40% of the mass of the ribosome consists of proteins, which are analyzable by differential scanning calorimeter (DSC) if the sample is suffi ciently concentrated. Ribosomal proteins are similar to many other proteins in that they are irreversibly denatured when heated, producing an endothermal effect that disappears upon reheating. In addition to ribosomes, bacterial cells contain other macromolecular components, such as the cell envelope, nucleic acids, and proteins. These components in whole cells go through conformational transi-tions upon exposure to heating in DSC. The transitions are recorded as endothermic (heat absorption) or exothermic (heat release) peaks in the thermogram. The area under the peak (enthalpy of transition, Δ H ) and the thermal stability (transition temperature, T m ), of each cellular component present on a typical DSC thermogram have been used to characterize bacterial cells.

The fi rst application of DSC on bacterial thermal analysis was the study on the physical properties of biomembranes. The physical prop-

Analysis of Foodborne Bacteria 149

erties of lipids in cell membranes of Mycoplasma laidlawii were inves-tigated by Steim et al. (1969) with DSC, by using whole cells, cell membranes, and extracted lipids. DSC thermograms of both isolated cell membranes and extracted membrane lipids showed an endother-mic transition around 40 ° C, suggesting extraction of lipids did not change the stability. However, whole - cell thermogram did not exhibit any distinguishable peaks (Bach and Chapman 1980 ).

The fi rst successful DSC on whole cells was the study on heat inac-tivation and spontaneous germination of bacterial spores. Maeda and colleagues (1974) observed that germinated Bacillus megaterium spores had endothermic peaks at about 100 ° C and 130 ° C. For vegeta-tive cells, Verrips and Kwast (1977) reported eight endothermic peaks on the whole - cell thermogram of Citrobacter freundii .

It is necessary to obtain distinguishable and reproducible transitions to identify the origin of the transitions and to examine their stability. The resolution of peaks can be enhanced by increasing viable cell density in the sample, by increasing sample size, and by improving the sensitivity of DSC instrument. Recent studies showed that larger and more distinguishable peaks can be obtained by using cell pellets instead of cell suspensions and by using the cells at a late logarithmic growth stage (Mackey et al. 1991 ; Lee and Kaletun ç 2002a,b ).

This chapter focuses on the characterization of bacterial inactivation by using DSC - relevant conditions on precooking, refrigerating, or high - pressure processing of food to ensure its safety.

C. perfringens and L. monocytogenes Analysis by DSC

DSC was used to examine changes in temperatures of endothermal effects of ribosomal proteins under cold - and heat - shocked conditions to determine thermal tolerance of ribosomes in C. perfringens and L. monocytogenes . In addition, L. monocytogenes cells were exposed to several antibiotics that bind to ribosomes to mimic cold - shock responses.

Sample Preparations

Enterotoxin - producing strains of C. perfringens were grown in fl uid thyoglycolate bacteriological medium. The ribosomes from C. perfrin-


gens were isolated according to procedure of Novak, Tunick, and Juneja (2001) . Harvested vegetative cells were concentrated by cen-trifugation and resuspended in buffer consisting of 25 mM Tris (pH 7.5), 1 mM EDTA (pH 7.5), 5 mM β - mercaptoethanol, 6 mM MgCl 2 , and 30 mM NH 4 Cl. Cells were broken in a French pressure cell at 82.7 MPa, and DNase was added. Pellets of crude ribosomes were produced by further centrifugation at 32,500 g .

Whole cells of L. monocytogenes were concentrated by centrifuga-tion and resuspended in buffer consisting of 10 mM Tris (pH 7.5), 6 mM MgCl 2 , and 30 mM NH 4 Cl using the procedure of Bayles et al. (2000) . Investigations of antibiotic - treated cultures were performed after exposing cells to antibiotics for 30 min at 37 ° C and then centri-fuging and resuspending in buffer. Antibiotics used included chloram-phenicol, erythromycin, kanamycin, puromycin, rifampin, streptomycin, and tetracycline (Sigma Chemical Co., St. Louis, MO). Cold shocking was performed by incubating at the specifi ed temperature for 3 h.

C. perfringens cells and ribosomes were analyzed using a Perkin - Elmer DSC - 7 equipped with an intercooler cooling accessory, and DSC of L. monocytogenes cells was performed in a Perkin - Elmer Pyris I with a liquid nitrogen cooler (Perkin - Elmer Corp., Norwalk, CT). Samples weighing approximately 12 – 20 mg were hermetically sealed in volatile sample pans, and the appropriate Tris buffer was used as a reference. After placing the pan in the instrument, C. perfringens samples were cooled to 10 ° C and L. monocytogenes samples were cooled to 0 ° C. After 2 min, samples were scanned to 100 ° C at 10 ° C/min, and the baseline obtained from scanning the sample a second time was subtracted, producing the fi nal curve. At least three replicate analyses of each sample were performed. Peak temperatures were calculated by using the instruments ’ software. Helium was used as the fl ow gas in both instruments, which were regularly calibrated with an indium standard.

Thermal tolerance studies and determination of D 60 values were conducted by dilution, submerged - coil heating, plating, and enumera-tion as described previously (Bayles et al. 2000) .

C. perfringens Results

A DSC scan of ribosomes isolated from C. perfringens vegetative cells is shown in Figure 7.1 , curve A. There was an endothermal effect with


a peak around 72 ° C, which corresponded to the 50S subunit and 70S particle (Miles, Mackey, and Parsons 1986 ). A shoulder at 66 ° – 67 ° C was due to the 30S subunit (Mackey et al. 1991 ). The peak and shoul-der disappeared with a subsequent scan without a change in baseline, proving that the ribosomal proteins were denatured by heat. The dena-turation peaks of whole cells kept at 46 ° C (heat - shocked) and 28 ° C (control) were several degrees higher (Figure 7.1 , curves B and C). The heat - shocked sample exhibited an increased resistance to heat, indicating that the structure or conformation of the protein was altered at elevated temperatures (Novak, Tunick, and Juneja 2001 ). Additional endothermal effects were observed around 81 ° – 85 ° C, which have been attributed to bacterial DNA (Miles, Mackey, and Parsons 1986 ). Storage at 4 ° C for several days, mimicking refrigeration in a

Figure 7.1. DSC of C. perfringens vegetative cells. Curve A, isolated ribosomal proteins; curve B, whole cells treated at 46 ° C for 60 min; curve C, whole cells treated at 28 ° C for 60 min; curve D, technique for B followed by storage at 4 ° C for several days; curve E, technique for C followed by storage at 4 ° C for several days.


supermarket or by a consumer, caused the peaks of both the heat - shocked and control samples to fl atten and shift to lower temperatures (Figure 7.1 , curves D and E ). The increased heat resistance of the heat - shocked cells was lost, supporting the theory that this resistance is transient (Heredia, Labb é , and Garc í a - Alvarado 1998 ). Heat is uni-formly distributed in a cell, resulting in damage to the most sensitive molecules within it. The results suggest that conformational changes in ribosomal proteins in response to temperature differences alter protein synthesis in C. perfringens and that refrigeration will destroy this organism in food. These conformational changes, which may involve changing the shape and structure of the protein, are readily discerned by evaluation of DSC scans.

L. monocytogenes Results

The DSC curve of L. monocytogenes cells (Figure 7.2 A) exhibited melting transitions at 67.5 ° ± 0.4 ° C, corresponding to thermal denatur-ation of the 30S subunit, and at 73.4 ° ± 0.1 ° C, corresponding to the combined 50S subunit and 70S particle (Bayles et al. 2000 ). Cold shocking the cells at 0 ° C for 3 h caused a shift in the 50S/70S peak denaturation temperature to 72.1 ° ± 0.5 ° C (Figure 7.2 B). The position

Figure 7.2. DSC of L. monocytogenes cells. Curve A, control grown at 37 ° C; curve B, grown at 37 ° C and cold shocked at 0 ° C for 3 h.


of the 30S peak did not shift signifi cantly. Similar results were observed with a cold shock to 5 ° C. Peak shoulders observed around 81 ° C were due to bacterial DNA (Miles, Mackey, and Parsons 1986 ), as with the Clostridium samples. The results indicate that intracellular changes in the ribosomes, such as an alteration in the association status of the 70S particles, are correlated with changes in the thermal properties of L. monocytogenes . The 30S and 50S subunits are more thermally labile than the associated 70S particle, so any change that causes dissociation of 70S would make the ribosome more sensitive to heat (Stephens and Jones 1993 ).

Effect of Antibiotics on Bacteria

Certain antibiotics inhibit protein synthesis by selectively targeting bacterial 70S ribosomes while leaving eukaryotic ribosomes unaf-fected (Weisblum and Davies 1968 ). The effects of seven antibiotics, six active against the ribosome and one (rifampin) active against RNA polymerase, were tested on the cells to determine whether the antibi-otic treatment produced alterations in peak denaturation temperatures corresponding to ribosomes or their subunits. Figure 7.3 , curve A, is

Figure 7.3. DSC of L. monocytogenes cells treated with antibiotic. Curve A, control; curve B, kanamycin - treated cells; curve C, tetracycline - treated cells.


the DSC curve of a control similar to that in Figure 7.2 , curve A, and Figure 7.3 , curve B, is the curve of cells treated with kanamycin. The 50S/70S peak shifted from 73.3 ° ± 0.1 ° C to 72.1 ° ± 0.7 ° C, a shift that was similar to the temperature reduction in cells that had been cold shocked (Figure 7.2 ). Treatment with tetracycline removed the 30S transition that had been observed around 67 ° C (Figure 7.3 , curve C). Thus, DSC analysis showed evidence of structural changes in the ribosomal protein. Treatment with chloramphenicol, erythromycin, puromycin, rifampin, or streptomycin produced results that were similar to those of the control.

Cells were also cold shocked from 37 ° to 0 ° C for 3 h and then thermally challenged at 60 ° C to determine thermal tolerance. Previous research revealed that the D 60 value of L. monocytogenes is 75.6 s (Miller, Bayles, and Eblen 2000 ). Kanamycin and tetracycline, which measurably altered the DSC curves of L. monocytogenes cells, were the antibiotics that caused reductions in thermal tolerance; chloram-phenicol, erythromycin, puromycin, rifampin, and streptomycin did not alter the D 60 values. Compared with the controls, kanamycin and tetracycline each reduced the D 60 value by 20 s. These 26% reductions were approximately the same as those observed following cold shocks of 37 ° – 0 ° C (Miller, Bayles, and Eblen 2000 ) and 37 ° – 5 ° C. The anti-biotic treatment data indicate that ribosomal changes have a signifi cant impact on the thermal resistance of L. monocytogenes . Cold shock and certain antibiotics alter the state and modify the structure of ribosomes, as refl ected by changes in the DSC curves. The results are probably due to disassociation of the 30S subunits, which are more thermally labile and more effectively denatured by heat.

Similar results were observed in Dr. Kaletun ç ’ s laboratory when erythromycin - treated E. coli cells were analyzed by DSC (Figure 7.4 ). E. coli cells suspended in HEPES buffer were treated with erythromy-cin for 40 min. With increasing concentration of erythromycin, it appears that major ribosomal transition shifts to a higher temperature in comparison with the thermogram of untreated cells. Furthermore, the shape of the peak changes and becomes less broad. Erythromycin is known to bind the 50S of bacterial ribosome, blocking the exit of the growing peptide chain, thus inhibiting the translocation of peptide. It can be speculated that treatment with erythromycin removes the 50S transition from the 50S/70S peak observed in the control cell thermo-


gram as one broad peak. The thermogram after a treatment at a 50 μ g/ml erythromycin level therefore shows the endothermic transition being shifted to a higher temperature because it belongs to denaturation of 70S ribosomes, which is expected to have the highest thermal stabil-ity among the ribosomal subunits.

E. coli and Lactobacillus plantarum Analysis by DSC

When microorganisms are heated in DSC, thermograms exhibit a number of overlapping transitions with a net endothermic effect (Miles, Mackey, and Parsons 1986 ; Anderson et al. 1991 ; Mackey et al. 1991 ; Mohacsi - Farkas et al. 1999 ; Lee and Kaletun ç 2002a ). Mackey et al. (1991) investigated the origins of apparent individual transitions on the thermogram of E. coli . Individual peaks observed in thermograms of whole cells of E. coli were assigned to cell components by comparing the transition temperatures of isolated cell components with corre-sponding transitions in whole cells.

Figure 7.4. Thermograms of whole cells of E. coli treated with erythromycin, control (thick dashes), 10 μ /ml erythromycin (thin dashes), 50 μ g/ml erythromycin (dots).


Sample Preparations

E. coli cells were grown in trypticase soy broth, and Lactobacillus plantarum cells were grown in MRS broth at 37 ° C to late exponential growth phase. The fi nal concentration of cells in the medium was 1.3 ± 0.1 × 10 9 cfu ml − 1 for E. coli and 9.0 ± 0.1 × 10 8 cfu ml − 1 for L. plantarum . The cells were harvested by centrifugation at 10,000 g for 10 min at 4 ° C. The supernatant was discarded and the pellets were washed with sterile distilled water and centrifuged for a second time before transferring into DSC crucibles.

A differential scanning calorimeter (DSC 111, Setaram, Lyon, France) was used to record thermograms of microorganisms heated at a 3 ° C min − 1 . All DSC measurements were conducted using fl uid - tight, stainless steel crucibles. For each DSC run, the reference crucible was fi lled with distilled water equivalent to the water content of the sample. After heating in the DSC, samples were cooled rapidly by liquid nitro-gen and rescanned to evaluate the reversibility of transitions. DSC thermograms were corrected for differences in the empty crucibles by subtracting an empty crucible baseline.

E. coli and L. plantarum Results

DSC thermograms for E. coli and L. plantarum whole cells are shown in Figure 7.5 (Lee and Kaletun ç 2002a ). The peaks on the thermograms correspond to the thermally induced transitions of cellular components. Several differences exist between the DSC thermograms of E. coli and L. plantarum . The major peak, peak a 2 , shows up at a higher tempera-ture in the E. coli thermogram (70 ° C) in comparison with the L. plan-tarum thermogram (63 ° C). Another visible difference between the E. coli and L. plantarum thermograms is a high - temperature endothermic transition (peak d) observed only in the DSC thermogram of E. coli whole cells. Based on the other DSC studies in Dr. Kaletun ç ’ s labora-tory for Gram - negative ( Pseudomonas fl uorescens ) and Gram - positive ( Staphylococcus aureus and Leuconostoc mesenteroides ) bacteria, Lee and Kaletun ç (2002a) suggested the origin of this peak is a cellular component of Gram - negative bacteria, more likely to be due to lipo-polysaccharide transition.

Lee and Kaletun ç ( 2002a ) also evaluated the thermal stabilities and the reversibility of individual transitions by a second temperature scan


after preheating in the DSC to various temperatures between 40 ° C and 130 ° C. They correlated with calorimetric data viability of bacteria subsequent to a heat treatment between 55 ° C and 70 ° C in the DSC. The fractional viability based on calorimetric data defi ned as the reduced apparent enthalpy [( Δ H – Δ H f )/( Δ H 0 – Δ H f )] and plate count data defi ned as ( N / N 0 ) show a linear relationship. Viability loss and the irreversible change in DSC thermograms of pretreated whole cells are highly correlated between 55 ° C and 70 ° C. Comparison of DSC scans for isolated ribosomes shows that the thermal stability of ribosomes from E. coli is greater than the thermal stability of L. plantarum ribo-somes, consistent with the greater thermal tolerance of E. coli observed from viability loss and DSC scans of whole cells. The denaturation of the ribosomal subunits occurred at the 50 ° – 80 ° C range in both ther-mograms. The result indicated that the ribosomal denaturation by the DSC was associated with the 30S and 50S ribosomal subunits in increasing order of thermal stability. This study demonstrated that calorimetric data can be used to evaluate the viabilities of microorgan-isms exposed to thermal treatments. Furthermore, the relative thermal stabilities of different organisms to heat treatment can be compared.

The calorimetric data also show that the heat denaturation of DNA might not be a major factor of vegetative cells ’ death because the event

Figure 7.5. Thermograms of whole cells of E. coli (dashes) and L. plantarum (dots) obtained by DSC (1 ° to 150 ° C with 3 ° C min − 1 heating rate). From Lee and Kaletun ç (2002a) .


is only partially irreversible and requires a higher temperature (85 – 100 ° C) than bacterial death (Lee and Kaletun ç 2002a ).

Application of DSC for Evaluation of Food - Processing Treatments

Food preservation treatments are used to inactivate microorganisms and to enhance the shelf life of food products. The food industry uses thermal processing as the main technology for food preservation. However, alternative thermal processes, nonthermal processes, and processes using mild heating in conjunction with antimicrobial agents also have been used to preserve nutritional and textural qualities of food materials. Preservation treatments affect cellular components of foodborne microorganisms, resulting in physiological changes in cells and eventually the death of bacteria. DSC thermograms of whole bac-terial cells exhibit differences in thermally induced transitions, reveal-ing the response of bacteria to heat. Thus, DSC technique allows one to monitor and to detect the impact of thermal treatment on cellular components of bacterial cells, including ribosomal subunits, nucleic acids, and cell wall components. The differences in ribosomal thermal stabilities of various bacteria are shown to be related to the thermal tolerances of bacterial cells to heat (Lee and Kaletun ç 2002a ; Mackey et al. 1993 ; Miles, Mackey, and Parsons 1986 ). In this section, we will focus on the quantitative evaluation of cell viability from calorimetric data and the evaluation of impact of nonthermal treatments using calo-rimetric data.

Determination of Heat Inactivation Parameters of Bacteria from Calorimetric Data

The effi cacy of a given treatment for inactivation of foodborne patho-genic and spoilage microorganisms depends on the inactivation kinetics of a target microorganism. In general, bacterial inactivation is consid-ered as a fi rst - order kinetics process. Therefore, the bacterial inactiva-tion kinetics can be described by the D value (the time needed to reduce the population by 1 log) and z value (temperature change required for a 1 - log reduction in D value. The D and z values are determined under isothermal conditions. However, in industrial applications, processing


temperature is reached over a period of time during that a signifi cant reduction of microbial population may occur as the temperature rises (Peleg 1999 ). Therefore, it is important to determine the D and z values under conditions similar to those used in processing.

There are several studies in the literature modeling microorganism inactivation during increasing temperature protocols (Reichart 1979 ; Thompson et al. 1979a,b ; Van Impe et al. 1992 ). DSC is ideally suited to achieve heat treatment under controlled conditions of linearly increasing temperature. Some investigators have used DSC to deter-mine the thermally induced transitions and to evaluate the relationship between the stability of cellular components and cell injury or death (Miles, Mackey, and Parsons 1986 ; Mackey et al. 1988, 1991, 1993 ). An equation describing the rate of microorganism inactivation as a function of linearly increasing temperature was used to determine the temperature at which the maximum death rate occurred for vegetative cells (Miles, Mackey, and Parsons 1986 ) and to predict the number of surviving microorganisms as a function of temperature at a constant heating rate (Miles and Mackey 1994 ). The results demonstrated that the temperatures required to inactivate L. monocytogenes increased with the heating rate. Miles and Mackey (1994) stated that the derived equation can also be used to calculate the D and z values under linearly increasing temperature protocols.

Lee and Kaletun ç (2002b) used a novel approach to obtain the kinetic parameters of E. coli K12 inactivation using calorimetric data. E. coli pellets were preheated in the DSC to preset temperatures, were cooled immediately by liquid nitrogen, equilibrated at 1 ° C, and were rescanned to 140 ° C. The rescan contained the thermally induced tran-sitions associated with the bacterial cells surviving after the preheat. Peak areas (apparent enthalpies, Δ H , J g − 1 ) corresponding to the con-tributions of survivors were determined from the apparent heat capac-ity versus temperature profi le by integrating the area under the curve (Figure 7.6 ).

Miles and Mackey (1994) derived a mathematical model describing the number of surviving cells under linear heating conditions (Equation 7.1 ).

ln ln . ln .− ⎛

⎝⎞⎠

⎡⎣⎢

⎤⎦⎥

= + −NN z

T zD r z

Te

e0

2 303 2 303

(7.1)


where N is the number of survivors at time t , N 0 is the initial number of viable cells, r is the heating rate, and D e is the D value at an arbitrary temperature T e . The value N / N 0 represents the fraction of survivors as a result of heat treatment.

Lee and Kaletun ç (2002b) , assuming that Δ H is proportional to the number of survivors, wrote Equation 7.2 to describe the fraction of surviving cells in terms of the DSC observable:

NN

H HH H

f

f0 0≈

−−

Δ ΔΔ Δ

(7.2)

By substituting Equation 7.2 into Equation 7.1 , Lee and Kaletun ç (2002b) obtained an equation that enables one to obtain kinetic param-eters of bacterial inactivation from calorimetric data.

ln ln . ln .−

−−

⎛⎝⎜

⎞⎠⎟

⎡⎣⎢

⎤⎦⎥

= + −Δ ΔΔ ΔH HH H z

T zD r z

Tf

f ee

0

2 303 2 303

(7.3)

This novel approach demonstrated that calorimetric data obtained with linearly rising temperature in DSC can be used not only for quali-tative evaluation of bacterial inactivation kinetics but also quantitative evaluation. The D and z values for E. coli K12 determined from the calorimetric data and the corresponding values from plate count data

–0.75

–1.00

–1.25

–1.50

–1.75

–2.00

–2.25

10 20 30 40 50 60 70 80 90 100 110 120 130

Figure 7.6. DSC thermogram for whole cells of E. coli K12 displaying curve baseline used to determine the apparent enthalpy value. From Alpas et al. (2003) .


obtained after heat treatment in the DSC and after isothermal treatment displayed close agreement. This approach provides reproducible and accurate results in a short time compared with the plate count technique because the DSC approach eliminates the incubation time normally used for plating, which might take 2 days or more.

Determination of Effi cacy of Nonthermal Treatments from Calorimetric Data

There is a growing interest in using techniques alternative to thermal processing for food preservation to enhance safety and shelf life of perishable foods (Hoover et al. 1989 ; Knorr 1993 ). Among nonthermal treatment processes, high hydrostatic pressure (HHP) appears to be the most promising technology. HHP processing has the advantage over conventional heat treatments in that, while this technique is effective in inactivation of non – spore - forming microorganisms, substantial food quality retention can be retained by avoiding the destruction of small molecular compounds such as vitamins.

It is reported that cell death increases as the level of the pressure applied increases, implying that critical cellular activities or processes have been irreversibly damaged (Hoover et al. 1989 ; Cheftel 1995 ). However, the pressure tolerance varies among the species of bacteria and even among the various strains of the same species (Styles et al. 1991 ; Patterson et al. 1995 ; Hauben et al. 1997 ; Alpas et al. 1999 ; Benito et al. 1999 ).

Although DSC is a thermal analysis technique, it has been applied to evaluate the impact of HHP processing on inactivation of bacteria by comparing the pre - and postprocess thermograms (Niven, Miles, and Mackey 1999 ; Alpas et al. 2003 ; Kaletun ç et al. 2004 ). The com-parison of various fi nal states as a function of various physical and chemical factors, starting from the same initial state, makes it possible to use DSC to predict the effectiveness of methods to inactivate microorganisms.

Niven and colleagues (1999) demonstrated by DSC studies that cell death due to high - pressure treatment may also be related to irreversible ribosomal damage. Alpas et al. (2003) confi rmed quantitatively that cell viability decreases as the extent of ribosomal denaturation assessed by calorimetry increases. The ribosomal denaturation was evaluated by comparing the total apparent enthalpy of the control and pressure -


treated cells and was related to the log reduction in viability. Furthermore, they demonstrated quantitatively that the relative sensi-tivities to high hydrostatic pressure treatment of bacterial strains from E. coli O157:H7 and S. aureus can be assessed from calorimetric data (Table 7.1 ). The results showed that pressure and thermal tolerances of bacteria can be different as can be the mechanism of denaturation.

Table 7.1. Apparent enthalpy and viability data for untreated control and pressure - treated cells.

Bacteria

Apparent enthalpy (J/g wet weight)

Fractional reduction in apparent enthalpy ( Δ H 0 – Δ H)/ Δ Δ H 0

Viable cells (cfu/ml)

Log reduction in viability − log 10 (N/N 0 )

S. aureus 485 Control

4.0 1.6 × 10 9 —

S. aureus 485 345 MPa

2.7 0.32 5.0 × 10 6 2.5

S. aureus 765 Control

3.8 2.0 × 10 9 —

S. aureus 765 345 MPa

2.4 0.37 1.6 × 10 6 3.1

E. coli O157:H7 933

Control

3.7 2.0 × 10 9 —

E. coli O157:H7 933

275 MPa

2.8 0.24 2.0 × 10 7 2.0

E. coli O157:H7 931

Control

3.7 1.3 × 10 9 —

E. coli O157:H7 931

275 MPa

2.7 0.27 6.3 × 10 6 2.3

From: Alpas et al. 2003


Whereas S. aureus 765 had a relatively higher resistance to thermal treatment in comparison with S. aureus 485, S. aureus 485 was deter-mined to be more resistant to pressure than S. aureus 765. This infor-mation can be used in the design of processes specifi c to targeting certain cellular components by using different physical stresses.

Determination of Impact of Antimicrobials on Bacteria from Calorimetric Data

Hurdle technology, which involves mild heating in conjunction with antimicrobial agents, has been used by the food industry to preserve nutritional and textural qualities of food while maintaining its extended shelf life (Leistner 2000 ). Acids, salt, and ethanol are the most com-monly employed preservatives used to reduce the intensity of the heat treatment (Cameron, Leonard, and Barret 1980 ; Adams et al. 1989 ; Casadei et al. 2001 ). The effectiveness of hurdle technology can be enhanced if hurdles target different cellular components, thereby reducing the tolerance of bacteria to heat treatment and preventing cellular repair mechanisms during the storage of the food product. DSC can be used to monitor changes in cellular components induced by chemical agents in vivo by comparing the thermograms of bacteria before and after treatment.

Lee and Kaletun ç (2005) investigated the infl uence of organic (acetic acid) and inorganic (hydrochloric acid) acids, ethanol, or NaCl treat-ment on the cellular components of E. coli by using calorimetry and compared the calorimetric data with viability results obtained by the plate count method. All chemical treatments resulted in shifting of ribosomal denaturation transition to a lower temperature, an indication of the increasing sensitivity of the bacteria prior to heat treatment. The comparison of the DSC thermograms of control cells with the thermo-grams of ethanol or acetic acid - treated cells showed, in addition to thermal stability decrease, a major reduction in size of the ribosomal subunit transition peak, which can be interpreted as the lower energy requirement for denaturation of ribosomes. The observed changes in the DSC profi les were irreversible and were associated with the loss of viability assessed by a plate count method. The decrease in thermal tolerance of the bacterial cell to heat treatment was chemical - specifi c and a function of the chemical concentration. The heat sensitivity of bacterial cells following an acid treatment was observed to be greater


than in the cells treated with ethanol and salt. Differences also were observed in the DSC profi les of bacterial cells treated with organic or inorganic acid, suggesting that the mechanism of reduced thermal tolerance of bacterial cells by these acids may be different. For design of hurdle technology application in food processing, DSC studies in vivo provide valuable information relevant to the effectiveness of hurdles.

Conclusions

DSC is a valuable tool when investigating the effect of physical or chemical treatments applied during food preservation on inactivation of bacteria. Among the cellular components in a bacterial cell, the damage to ribosomal proteins due to thermal, nonthermal, chemical, or antibiotic treatments appears to be related to loss of cell viability. DSC scans show that protein synthesis in C. perfringens and L. mono-cytogenes ribosomes is more effi ciently destroyed during heating when conformational changes and disassociation of the 30S subunits are induced by temperature shocks. DSC thermograms display information about the cellular components affected by various preservation treat-ments, thereby providing insight into the mechanism of bacterial inac-tivation. Furthermore, the calorimetric data can be analyzed to obtain quantitative information about bacterial inactivation, including thermal stability, thermal energy required for bacterial inactivation, and the kinetic parameters of inactivation. Calorimetric data can be used to optimize the processing conditions of food preservation in a rational manner.

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Mackey , B.M. , Parsons , S.E. , Miles , C.A. and Owen , R.J. 1988 . The relationship between base composition of bacterial DNA and its intracellular melting tempera-ture as determined by differential scanning calorimetry . J Gen Microbiol , 134 : 1185 – 1195 .

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Stephens , Peter J. and Jones , Martin V. 1993 . Reduced ribosomal thermal denatur-ation in Listeria monocytogenes following osmotic and heat shocks . FEMS Microbiol Lett , 106 ( 2 ): 177 – 182 .

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Chapter 8

Coupling of Differential Scanning Calorimetry and X - Ray Diffraction to

Study the Crystallization Properties and Polymorphism of Triacylglycerols

Christelle Lopez , Daniel J.E. Kalnin , and Michel R. Ollivon *

169

Introduction 169 Thermal and Crystallographic Properties of Triacylglycerols 170

Polymorphism of Triacylglycerols 170 Differential Scanning Calorimetry 173 X - Ray Diffraction 175

Coupling of XRD and DSC: MICROCALIX 176 Applications and Results 179

Cocoa Butter and Its Components 179 Milk Fat 184 Lard 190


Introduction

Lipids from vegetable or animal origins are widely consumed in food products, for example, in chocolate, shortenings, margarine, and butter. The composition of triacylglycerols (TG), which are the main constitu-ents of natural fats and oils or hydrogenated and interesterifi ed fats (i.e., such as in margarine), their suprastructure, and the physical prop-

* This chapter is dedicated to Michel Ollivon who passed away on June 16th, 2007.


erties of fat determine the mouth - feel, fl avor release, and functional properties of high - fat content food products. Moreover, the lipid phase of food stuff is sometimes partially crystallized at the temperature of storage (in a freezer or fridge at − 20 and 4 ° – 7 ° C, respectively) and consumption, including cocoa butter in chocolate, hydrogenated mar-garine, and milk fat in dairy products.

Studying the properties of TG is important to better understand and then control the physical properties of fats. Increasing the knowledge of both the physical and thermal properties of fats (i.e., solid fat content and type of crystals as a function of temperature) in anhydrous state as well as in situ in food products is of tremendous importance with respect to functional, sensorial, and nutritional properties. Moreover, the increased knowledge of TG crystallization and polymorphism in fats is of value for technical applications as well as for the development of new processes and products.

The polymorphism of TG renders the study of the thermal and structural properties of lipids very complex. Both types of properties, largely depending on sample history, are conveniently determined using differential scanning calorimetry (DSC) and X - ray diffraction (XRD) techniques. In this chapter, we focus on the thermal and crystal-lographic properties of TG investigated by these two techniques, which are coupled using the microcalorimeter MICROCALIX.

Thermal and Crystallographic Properties of Triacylglycerols

Polymorphism of Triacylglycerols

Fatty acids have various melting points, which mainly depend on the number of carbon atoms and their level of unsaturation (Table 8.1 ). The melting point of a TG molecule (triester of fatty acids and glyc-erol) depends on the three fatty acids esterifi ed and on their position on the glycerol (sn - 1, sn - 2, sn - 3) (Table 8.1 ).

Thus, the thermal behavior of natural fats, constituted by several types of TG molecules, is really complex. Moreover, the assignment of the thermal properties of fats is complicated by the existence of a polymorphism of monotropic type for each TG (Small 1986 ; Ollivon and Perron 1992 ).

Each TG can exhibit several crystalline forms, the occurrence of which strongly depends on its thermal history. Each polymorphic form

Coupling of Differential Scanning Calorimetry 171

of a given TG molecule is characterized by its own melting point (Table 8.2 ). Then, TG mixtures exhibit multiple melting points depend-ing on their composition, which makes the overall melting behavior of fat even more complex because some TG can cocrystallize.

TG polymorphism relates to the ability of molecules to arrange themselves within a crystal lattice in several different ways of lateral packing of the fatty acid chains (Figure 8.1 B) and of longitudinal stack-ing of molecules (Figure 8.1 B) in lamellar structures (Hagemann 1988 ). Thus, pure TG and mixtures of TG can adopt several crystalline arrangements. TG molecules have a polymorphism of monotropic type, which means that the transitions between the polymorphic forms are irreversible and are only possible from the least to the most stable species as characterized by a higher melting point. Moreover, poly-morphic transitions are only possible by way of a liquid phase (Small 1986 ). The three main polymorphic forms frequently observed for the lateral packing of fatty acid chains correspond to different subcells that

Table 8.1. Melting point of the main fatty acids found in natural fats and oils and of triacylglycerols.

Fatty Acids Triacylglycerols

Formula Name (Abbreviation)

Melting Point ( ° C)

TG Abbreviation

Melting Point ( ° C)

C4:0 Butyric acid (B)

− 8 BBB − 75

C6:0 Caproic acid − 4 OOO 5 C8:0 Caprylic acid 16 StOO 24 C10:0 Capric acid 31 StStO 38 C12:0 Lauric acid

(L) 44 StOSt 44

C14:0 Myristic acid (M)

54 PPO 34

C16:0 Palmitic acid (P)

63 POP 36

C18:0 Stearic acid (St)

70 LLL 47

C18:1 9c Oleic acid (O) 16 MMM 58 C18:2 9c, 12c Linoleic acid − 5 PPP 66 C18:3 9c,

12c, 15c Linolenic acid − 14 StStSt 73


have been described in detail (Small 1986 ; Ollivon and Perron 1992 ): hexagonal ( α form), orthorhombic perpendicular ( β ′ form), and tri-clinic parallel ( β form) (Figure 8.1 B). The density, enthalpy of fusion, melting point, and stability increase in the order α , β ′ , and β , according to the monotropic character of the polymorphism. In the α form, the lateral packing of the fatty acid chains is not very tight and the chains have considerable rotational freedom, whereas in the β form, the chains are very densely packed. TG crystals are made by the stacking of TG molecules layers, the thickness of which depends on the length and unsaturation of the fatty acid chains and their angle of tilt with respect to the basal planes formed by the methyl end groups of the TG (Figure 8.1 C). The longitudinal organization of TG in lamellar structures is primarily related to the number of chains stacked in the crystalline cell. For TG in natural fats, the number of fatty acid chains frequently observed is two or three and corresponds to the stacking of double (2L) - or triple (3L) - chain length lamellar structures (Small 1986 ; Hagemann 1988 ). Roughly, 3L forms are usually related to low - melting, long - chain monounsaturated and mixed long - and short - chain

Table 8.2. Some crystallographic and energetic properties of the three main polymorphic forms of a selection of TGs.

Property of TG *

Polymorphic Form

Hexagonal α Orthorhombic Perpendicular β ′ Triclinic Parallel β

Main short spacings ( Å )

4.15 3.8 and 4.2 4.6

Melting point ( ° C) of StStSt

55 64 72

Enthalpy of fusion (J g − 1 ) of StStSt

163 180 230

Melting point ( ° C) of OOO

− 32 − 12 5

Melting point ( ° C) of LLL

15 35 46

Melting point ( ° C) of POP

21 30 36

* TG, triacylglycerols; St, stearic acid; O, oleic acid; L, lauric acid; P, palmitic acid. Adapted from Mulder and Walstra 1974 .


TG, whereas 2L forms are generated mostly by similar long - chain, high - melting, trisaturated TG (Small 1986 ).

The techniques most frequently used for the study of the thermal and crystallographic properties of TG are DSC and XRD.


The thermal properties of fats are generally studied using DSC. In DSC, the difference between the heat fl ow (J/s or W) of a reference and a sample is measured as a function of temperature or time while they are subjected to a controlled temperature – time program. DSC is thus a form of differential thermal analysis (DTA). In any DSC instrument, the sample and reference are placed in small individual pans or cruci-

Figure 8.1. Main types of triacylglycerol (TG) packings. (A) Lamellar structure formed by TG molecules in the solid state: for example, β form of trilaurin. (B) Left: The stable conformation of the hydrocarbon chains of saturated fatty acid (FA) is a planar zigzag shown here as a 3D view along its main axis. Right: Three main types of lateral chain packings (only carbon atoms are drawn): hexagonal, orthorhombic perpendicular (O + upside down T), and triclinic parallel (T//) subcell in the order of their stability, which are named α , β ′ , and β for TG. (C) Two main types of TG longitudinal chain stacking (fatty acids are drawn as straight lines), 2L and 3L.


bles, which may be opened or hermetically sealed, of 10 – 100 μ l capac-ity. Because sample size is small, accurate weighing is essential for quantifi cation of the thermal properties of fats. The reference is often an empty pan, so as to not exhibit thermally induced transitions within the temperature range of interest. Transitions that occur in the sample during the applied temperature program appear as peaks or troughs, depending on whether they are exothermic or endothermic, on the plot of differential heat fl ow versus temperature or time (the thermogram) that is the output of the instrument. Conventionally, the temperature program used in DSC is a linear change in temperature with time, with various cooling and heating rates. After heating a lipid sample to 20 ° C over its fi nal melting point to erase its thermal history/memory (Ollivon and Perron 1992 ), different rates of cooling permit the investigation of the crystallization properties of TG, polymorph formation, and trans-formation. Cooling and heating can also be performed from a tempera-ture at which the fat is partially crystallized (e.g., 4 ° C for milk fat). Isothermal DSC, in which the temperature is kept constant at a value at which a transition of interest is known to occur, is especially useful for studying the polymorphic evolutions as a function of time (Lopez et al. 2002a ). The crystallization properties of fats depend on their thermal history and on the rates of cooling and heating applied. A combination of cooling, heating, and isothermal DSC scans can be used to study polymorphism of TG and fats in complex products.

DSC is a useful tool (1) to record the crystallization and melting profi les recorded on cooling and heating, respectively; (2) to determine the characteristic temperatures, such as the temperatures of initial crystallization ( T onset ) and fi nal melting ( T offset ); (3) to monitor polymor-phic evolutions and measure the heat of transitions; and (4) to quantify the solid fat content that is proportional to the enthalpy of melting ( Δ H ) of fat.

The effects of different rates of cooling and heating on polymorph formation and polymorphic transitions recorded as a function of tem-perature or time are easily studied by DSC. DSC studies have given an insight into the thermodynamics of fat phase transition in bulk and in emulsions. However, because the complex DSC recordings are often diffi cult to interpret and it is not possible to identify unequivocally polymorphs using DSC, the experiments must be coupled with other techniques, such as XRD or other techniques yielding structural infor-mation. Moreover, DSC allows the characterization of the physical


state changes if a change of energy is involved. However, this tech-nique does not provide information on the structure that exists before and after the phase transition.

X - Ray Diffraction

XRD is a powerful technique to use to provide structural information. As explained above, characteristic lengths of structures formed by TG range from atomic distances up to hundreds of å ngstroms. The whole size range can be investigated by means of X - ray scattering. Generally, X - ray scattering refl ects periodical differences of the electron density within a sample. Thus, X - rays are the ideal direct probe for determin-ing the internal structure of crystalline material because they provide information on the repetitive patterns of the electron density of the array of atoms. In their solid state, TG molecules are arranged periodi-cally in planes at repetitive distance d , which can be identifi ed using XRD (Figure 8.1 A).

Wide - angle X - ray scattering (WAXS) monitors the structure at atomic scale (from about 1 to 10 Å ). It provides information on intra - and intermolecular distances called short spacings. For crystallized TG, the hydrocarbon chains are arranged in regularly spaced planes. Crystallographic planes give rise to a refl ection line at a distinct angle θ , satisfying the well - known Bragg relation: 2 d sin θ = n λ , where λ is the X - ray wavelength, d is the repetitive distance between planes, n is an integer, and θ is half the angle between the incident and diffracted beam (Guinier 1964 ; Small 1986 ).

The actual data registered during an XRD experiment is the scat-tered intensity as a function of 2 θ . It is often convenient to use the scattering vector q instead of the scattering angle 2 θ because the former is independent of the wavelength of the incident beam. They are related as follows: q = (4 π / λ ) sin θ . Thus, the distance d between planes can be deduced from the position q of the diffraction peak by d = 2 π / q .

The different packing of aliphatic chains in the three crystalline TG subcells leads to characteristic wide - angle X - ray refl ections enabling their identifi cation. Short spacings are widely used for identifying the various crystalline subcells characterizing the polymorphic forms (Figure 8.1 B). A single line around 4.15 Å characterizes the α form (hexagonal subcell). A strong line at 4.6 Å and two small lines at about 3.85 and 3.7 Å identifi es the β form (triclinic parallel subcell), whereas


the β ′ form (orthorhombic subcell) pattern exhibits two lines at about 4.2 and 3.8 Å .

Small - angle X - ray scattering (SAXS) monitors the lamellar organi-zation (e.g., 2L or 3L) of a TG sample in a range from 10 to about 1000 Å . Refl ection maxima appear in the scattering profi le. The Bragg relation applies again, where d yields the mean thickness of adjacent lamellae (called TG long spacing). From the measurement of d ( Å ) and with the knowledge of the fatty acid composition (chain length, unsatu-ration), it is then possible to deduce if the stackings correspond to 2L or 3L organization (Figure 8.1 C).

In conclusion, the two levels of organization of crystallized TG, for example, the lateral packing of the fatty acid chains and the longitudi-nal stacking of TG molecules in lamellae, are easily identifi able from the short and long spacings observed by X - ray scattering at wide and small angles, respectively.

Recent use of synchrotron radiation, which provides X - ray fl ux 10 3 – 10 6 times more intense than that generated by usual X - ray sources, permits recordings to be performed in times ranging from a few mil-liseconds to seconds. Thus, direct continuous recordings can be achieved as a function of time (XRDt) or temperature (XRDT). Moreover, synchrotron radiation permits studying the organization of TG in water - dispersed systems such as emulsions and complex food products and to quantitatively monitor phase changes within emulsion droplets. This is especially interesting when relating the textural and rheological properties of fats and high - fat food products to the thermal and crystallographic properties of TG. Therefore, the functionality of fat in many food products cannot be understood without knowing both its composition and physical properties their dependencies.

Coupling of XRD and DSC : MICROCALIX

A new differential microcalorimeter, called MICROCALIX, has been developed within Centre National de la Recherche Scientifi que (CNRS) group UMR 8612 (Ollivon et al. 2006 ) to perform simultaneous thermal and X - ray measurements (Figure 8.2 ). Small volumes of samples (from about 1 to 20 μ l) are loaded in glass or quartz capillaries (external diameter 1.5 mm) ensuring minimum attenuation of the X - ray beam and parasitic scattering. The microcalorimeter allows, in its last version,


thermal scans in the temperature range − 30 ° C to +230 ° C, with scan-ning rates between 0.01 ° and 10 ° C/min and with sensitivity compa-rable with that of a modern commercial apparatus ( > 100 μ V/mW). Scanning temperature is controlled with a resolution of 0.01 ° C, and the microcalorimeter is calibrated with lauric acid.

MICROCALIX was used on synchrotron radiation X - ray benches. The lastest version of the instrument has been adapted for laboratory bench and conventional source, but it is preferably used with rotating anode or multilayered mirrors.

MICROCALIX inserted in a laboratory conventional X - ray source The microcalorimeter can be installed on a laboratory source designed for simultaneous SAXS and WAXS measurements, such as that of CNRS group at UMR 8612 in Ch â tenay - Malabry (France; Lopez et al.

Figure 8.2. Experimental setup of the microcalorimeter MICROCALIX in the time - resolved synchrotron XRD environment. (A) Schematic representation: The cell is positioned with the capillary containing the sample perpendicular to the beam in such a way that the diffraction patterns are recorded in the vertical plane by one or two one - dimensional proportional detectors (LD) at small and wide angles. Counting electronic (Counting Elect.), nanovoltmeter (nVmeter), and temperature controller (T Ctrl) are monitored by a single computer. The temperature - controlled cryostat (TCC) is kept at constant temperature (e.g., 6 ° C). (B) Setup on the D22 bench of synchrotron (LURE, Orsay, France). (C) Setup on the D24 bench of synchrotron (LURE, Orsay, France).


2008 ). The X - ray source of this setup is a Diffractis 586 generator (ENRAF - NONIUS) equipped with a long - fi ne - focus, Cu anode, sealed tube operated at 40 kV and 20 mA. CuK α ( λ = 1.54 Å ) radiation is selected and the line focused by a graded, elliptically bent, multilayer mirror (OSMIC - Rigaku, Troy, Michigan). SAXS and WAXS patterns are recorded by two linear, position - sensitive, gas detectors using ASA2.4 software (HECUS - Braun, Graz, Austria). The detector record-ing SAXS data is placed at the focus point of the mirror. The scattered intensity is reported as a function of the scattering vector q = 4 π sin θ / λ , where θ is half the scattering angle and λ the wavelength. The detectors are calibrated at wide angles with the crystalline β form of high - purity tristearin (characteristic repeat spacing 4.59, 3.85, 3.70 ± 0.01 Å ) (Ollivon and Perron 1992 ) and at small angles with silver behenate (long spacing of 58.380 ± 0.001 Å ) (Blanton et al. 2000 ). At small angles, this instrument covers the range of scattering vectors 0.05 < q < 0.4 Å − 1 and is thus well suited for measurements on lipids. Line focusing of the beam increases the fl ux on the sample in such a way that measuring time can be reduced to a few minutes (about 2 min), allowing study of lipid phase transition kinetics and mechanism at a rather low scanning rate.

MICROCALIX inserted in synchrotron radiation XRD bench The availability of a synchrotron radiation source with a brilliant beam of variable wavelength with very small vertical angular divergence has opened new opportunities. The high collimation of the beam allows the investigation of much larger structural features, with better spatial resolution. Increase in the fl ux by several orders of magnitude, com-pared to fl ux of conventional sources, enables the study of very dilute or weakly scattering samples such as TG emulsions (Lopez et al. 2007 ) and aerated food products (Kalnin et al. 2002 ). For bulk TG samples, time - resolved measurements can be performed with a time resolution down to a few milliseconds; thus, possible intermediate states in the course of a transition can be assessed (Lopez et al. 2006b ).

Generally, the timescale that may be reached during a time - resolved experiment depends on instrumental factors such as source and detec-tor characteristics and sample properties. For conventional sources, the time resolution is usually determined by the fl ux at the sample, whereas for synchrotron radiation source it is rather limited by the counting rate of detectors, because statistically signifi cant information must be col-


lected. Note that the time resolution is also limited by the heat con-ductivity of the sample. If the high fl ux of synchrotron radiation source is used to follow very fast transitions, very good heat transfer to a small sample must be ensured.

Experiments using MICROCALIX have been carried out at three synchrotron radiation sources, Laboratoire pour l ’ Utilisation du Rayonnement Electromagn é tique (Orsay, France), European Synchrotron Radiation Facility (Grenoble, France), and Elettra (Trieste, Italy); further experiments are planned at SOLEIL (Saclay, France).

Applications and Results

Examples of the study of crystallization properties and polymorphism of TG in natural fats and complex food products are presented below as major applications of the coupling of DSC with XRD.

Cocoa Butter and Its Components

Polymorphism of cocoa butter (CB), which is a vegetable fat used mainly by chocolate manufacturers, has often been discussed in the literature because it is related to the organoleptic and physical charac-teristics of the fi nal products (snap, molding contraction, gloss, and blooming during the storage). In fact, the quality of chocolate bars and pralines strongly depends on their physicochemical properties and on the polymorphic form of CB.

The polymorphism of CB can be compared with that of the main TG, POP, POSt and StOSt (where P is palmitic acid, O is oleic acid, St is stearic acid), which represent about 17%, 37%, and 27% of CB composition, respectively, and that of their mixtures (Wille and Lutton 1966 ; Kunutsor and Ollivon 1983 ; Sato 1987 ; Sato et al. 1989 ; Arishima et al. 1991 ; Loisel et al. 1998a ). CB polymorphism is commonly described in the literature in terms of six different polymorphic forms, noted as forms I to VI in Figure 8.3 (which are in fact sub - α , α , β ′ , and β forms) in increasing order of melting points, according to the nomenclature of Wille and Lutton (1966) . These six forms have been confi rmed by other authors (Loisel et al. 1998a ; Chapman et al. 1971 ; Adenier et al. 1975 ; Huyghebaert and Hendrickx 1971 ; Lovegren et al. 1976 ; Merken and Vaeck 1980 ; Davis and Dimick 1986 ). However,


the existence of some of them is debated, as well as the fact of the purity of some forms, especially I, III, and VI. In fact, it is very hard to obtain monocrystalline samples; rather, one obtains polycrystalline powders from pure TG as a solid state (Van Malssen 1994 ). Thus, it should be considered that complex mixtures of TG will occur either at a molecular level. It also has to be considered that lipid crystals of sub - α , α , and β ′ do not necessarily correspond to one homogenous crystalline state (Marangoni and McGauley 2003 ). Lipid crystals can be seeded to achieve the commercially desired form V (Figure 8.3 ) (Davis and Dimick 1989a,b ; Chaiseri and Dimick 1995a,b ). However, it is apparent from Figure 8.3 that this crystalline form is not the most stable one, and it is necessary to avoid polymorphic transition toward the more stable form VI, which is responsible for fat bloom. This can be achieved by the addition of minor components such as glycolipids, phospholipids, and saturated TG, which promote the

Figure 8.3. Summary of the possible arrangements of cocoa butter. Upper left shows a table resuming the notation, melting point, lateral and longitudinal packing of TGs. An overlay of two DSC recordings shows the closeness of two thermal events, notably the occurrence for V and form VI. Underneath is the XRD pattern of what is believed to be the pure crystalline species of forms V to VI.


crystallization of CB. However, good practice has to be used during tempering since seeding can also hinder formation of the desired form V of CB.

To better understand the phase behavior of CB, a MICROCALIX calorimeter has been used, together with synchrotron radiation, for the characterization of coexisting organizations resulting from the phase separations of TG and to follow the competition between the different polymorphic species quantitatively, even at fast scanning rates.

Cocoa butter polymorphism Using temperature - resolved XRD with MICROCALIX, it has been shown that phase separation systematically occurs during CB crystallization (Loisel et al. 1998 ). A trisaturated fraction of TG par-tially phase - separates from the mono - and polyunsaturated TGs by crystallizing fi rst on cooling. Segregation of TG molecules in the solid state has been observed when CB crystallizes under the forms II and V (the form under which chocolate is usually commercialized). It also can occur with some other forms, such as VI (Loisel et al. 1998a ). This behavior results from the poor solubility of trisaturated TG within the monounsaturated ones. The use of MICROCALIX for the monitor-ing of the formation and then transformation of the different forms, including form III, during heating of CB confi rmed unambiguously the results of phase transitions previously reported by Wille and Lutton (1966) .

Other approaches for obtaining the desired form V have been under-taken using the knowledge of these phase transitions upon heating and using the infl uence of shear and additives (Loisel et al. 1997a , 1998b ), providing a better understanding of the polymorphism of CB. The resulting knowledge is also relevant for the study of fat bloom (Loisel et al. 1997b ), which must be hindered by means other than additives.

The very fast cooling (about 100 ° C/s) of melted CB results in the formation of a phase that is less organized than the α form, since it transforms irreversibly into the latter on heating (Loisel et al. 1998a ). It is believed that metastable forms can be used for the rapid formation of desired polymorphic form V. Partly liquid crystalline structure, in which hydrocarbon chains are organized as in a “ brush ” may occur even in a partially crystalline state (Loisel et al. 1998a ). Such an orga-nization also can be compared with that shown by phospholipids, such


as phosphatidylcholine, in the liquid crystalline state (L α ). This degree of freedom in the molecular moiety left by the liquid part of the orga-nization allows the other moiety to crystallize very rapidly and into a very compact subcell. On the other hand, the presence of a liquid crystalline moiety in the structure would also explain its progressive transition into the α form, since it is well - known that the presence of a liquid favors the transition of unstable species toward more stable forms. This is well - known for fats (Timms 1984 ). This preorganization assumes that (1) the liquid state of CB, as other liquid fats, is liquid crystalline and already organized in lamellae in their liquid state (van den Tempel 1979 ), and (2) its organization corresponds to layers (or fl at aggregates) made from saturated chains while other parts contain the unsaturated ones.

Polymorphism of 1,2 - dipalmitoyl - 3 - oleoylglycerol As an example of the rapid liquid - mediated phase transitions, the heating of a crash - cooled sample of 1,2 - dipalmitoyl - 3 - oleoylglycerol (PPO) illustrates the importance of using the coupled techniques such as MICROCALIX (Ollivon et al. 2006 ). With the cooling of PPO from a melted state at 65 ° C down to 0 ° C, about 20 mg of pure PPO (99%) at the rate of 5 ° C/min leads to the crystallization of a metastable crys-talline variety (type 3L α ). On heating, this metastable form transforms into more stable varieties (Figure 8.4 , bottom). The DSC thermograms and the XRD patterns observed at small and wide angles on heating at 1 ° C/min are presented in Figure 8.4 . Four steps can be identifi ed on both DSC and XRD traces (Figure 8.4 , middle). Three endothermic and one exothermic event are observed in the domain 20 ° to 40 ° C on the DSC heating scan. The metastable structure (3L α ) initially formed with a period of about 75 Å (only order 2 line at 37.6 – 37.7 Å is shown on Figure 8.4 ) transforms into a mixture of two forms (both of β ′ type). One form has intermediate stability and the other form with smaller d spacing is stable (close to 33 and 40 Å ). The two forms melt consecu-tively (vertical dashed lines delimit the domain of existence of phases; Figure 8.4 , middle). DSC thermograms display several overlapping thermal events. As a consequence, in the intermediate part of this double transition, between the maximum of both peaks, only the result of both phenomena is recorded and schematically explained (Figure 8.4 , top). Such behavior is frequently observed in monotropic systems during melting of metastable crystalline forms when more stable nuclei


formerly entrapped in this metastable variety are allowed to grow at the expense of the metastable variety. This type of complicated thermal recording can be elucidated only if coupling of a structural technique is provided.

By using MICROCALIX, it is confi rmed that the transition toward the more stable crystalline form goes through an intermediate stability form (Ollivon et al. 2006 ). This monotropic transition is liquid medi-ated. This intermediate form, more stable than the initial form, melts between 27 ° and 30 ° C. A schematic of the proposed mechanism of

unstableforma (3L) 75 Å

intermediateformb’2 (2L) 39–41 Å

stable formb’1 (3L ?) 64–66 Å

20°C

35°C

liquid30°C

21°C50

0 10 20 30 40 50

40

30

32

1

4

20

10

00 1000 2000

time (sec) temperature (°C)

30000

5

6045403530

4

3

2

1

4

endo

3

2

1

2520151050

0.1 0.2 0.3 1.4 1.6 1.8 –100 –50 0 50 100

10

15 42

37

32

Tem

pera

ture

(°C)P

eak A

rea (

A.U

)

Long S

pacin

gs (

Å)

32

1

4

q (Å–1) q (Å–1) Heat flow (mV)

50454035302520151050

50

40

30

20

10

0

Tem

pera

ture

(°C

)

Figure 8.4. MICROCALIX recording of SAXS/WAXS and DSC. Heating from 0 ° to 50 ° C at 1 ° C/min of a 20 - mg sample of PPO. Three - dimensional representation of the evolutions of diffraction patterns recorded at small (left) and wide (middle) angles and shown as intensity as a function of scattering vector q and temperature T during the heating of sample. The four types of structures that are clearly visible in the domain 0 ° to 50 ° C are delimited by the transitions evidenced. The line corresponding to order 2 of the structural period of 75 Å is voluntarily cut to allow a better visualiza-tion of other lines at high temperature.


the fi rst reaction is seen in Figure 8.4 (top); however, mechanism of the second reaction is more complex. Only the quantifi cation of the evolution of the line intensities (Figure 8.4 , middle), corresponding to the long spacings and measured as peak surface areas, allows to inter-pret the second transition. It can then be seen that two forms coexist in the 20 ° to − 30 ° C range, while the periods of the crystalline arrange-ment change (Figure 8.4 ). In fact, the mechanism of transition is similar to the preceding one, except no exothermic event is recorded. The existence of a liquid crystalline phase obtained by very fast cooling was not found in pure TG such as triolein, tristearin, or even POP, which is nevertheless one of the major constituents of CB, because they all readily crystallize in α phase.

The usefulness of MICROCALIX is underlined by the explanation of complex phase changes as they frequently occur in lipids. Only clear attribution of thermal events as they are given in the examples above can lead to a thorough understanding of polymorphism of lipids and other materials showing polymorphism.

Milk Fat

Milk fat is consumed in dairy products, that is, milk, cream, whipped cream, cheeses, and butter, and also in powders, pastries, and cooked foods. Milk fat can in be partially crystallized form (e.g., a mixture of crystals and oil) over a wide range of temperatures, including the tem-perature of storage (4 ° – 7 ° C) and consumption. This thermal behavior results from its fatty acid composition and polymorphism of TG. Milk fat is the most complex fat found in nature, with more than 400 dif-ferent fatty acids (about 70% of saturated fatty acids and 25% of monounsaturated fatty acids, mainly oleic acid) and 200 different TG identifi ed.

DSC and XRD studies have given an insight into the thermodynam-ics of milk fat phase transition in bulk (Timms 1980 ; Lavigne 1995 ; ten Grotenhuis et al. 1999 ), in emulsions (Lopez et al. 2002b ), and in complex food products such as cheese (Rowney et al. 1998 ; Famelart et al. 2002 ; Lopez et al. 2006a ).

Anhydrous milk fat Anhydrous milk fat (AMF), which is the fat isolated from butter, has a broad melting range, from − 40 ° C to +40 ° C, and no true melting


point as do pure compounds. DSC was used to demonstrate the presence of polymorphism in anhydrous milk fat (Mulder and Walstra 1974 ). When polymorphism is present, the thermograms for samples of the same fat preconditioned thermally in different ways will have different features. DSC studies of anhydrous milk fat show that it crystallizes and melts in several steps (Lopez et al. 2007 ). A typical melting curve of AMF shows three endothermic peaks, corresponding to low melting point (LMP), medium melting point (MMP), and high melting point (HMP) fractions (Timms 1980 ). These peaks correspond to large groups of TG that melt separately and behave as solid solutions. The number of thermal transitions in DSC thermograms, the partial overlapping of the melting peaks, and their respective enthalpies and transition temperatures, depend strongly on the thermal treatments (e.g., heating and cooling rates, tempering) and on the entire thermal history of the sample (Ollivon and Perron 1992 ; Ali and Dimick 1994 ).

Recently, the use of DSC coupled to synchrotron radiation XRD with MICROCALIX allowed identifi cation of the crystalline struc-tures formed by TG molecules as a function of temperature and time in anhydrous milk fat (Lopez et al. 2001a,b, 2005 ) and its frac-tions (Lavinge 1995 ; Lopez 2006b ). The samples were melted com-pletely (heated to 60 ° C for 5 min) to ensure that all crystals and nuclei were melted and to erase the thermal history of fat. The samples were then cooled with cooling rates in the range of 0.15 ° C.min − 1 ≤ R cooling ≤ 1000 ° C.min − 1 . The most rapid R cooling was obtained by rapid introduction of the capillary into the calorimeter MICROCALIX precooled to the temperature, for example, 4 ° C. Tempering in isother-mal conditions were also performed, for example, at − 8 ° C, 4 ° C, and 20 ° C. Then, XRD patterns were recorded as a function of time and on subsequent cooling or heating (in general at 2 ° C.min − 1 ). Figure 8.5 shows the crystallization curve of anhydrous milk fat recorded on cooling at 1 ° C/min, with the XRD patterns recorded as a function of temperature, which allows the relation between the thermal events and the crystalline structures.

The crystallization properties of milk TG were studied after quench-ing ( ∼ 1000 ° C.min − 1 ) to characterize the most unstable crystalline structures and their reorganization as a function of time (Figure 8.6 ). The samples were cooled rapidly from 60 ° C to 4 ° C to ensure crystallization of fat, and after temperature equilibration, the thermal


properties were investigated as a function of time under isothermal conditions. Isothermal DSC was performed, with the temperature being kept constant at 4 ° C to study the thermal and the structural properties of TG. The heat of crystallization released as a function of time resulted in exothermic signals corresponding to the polymorphic evolution in the fat ( α to β ′ polymorphic transition as indicated in Figure 8.6 ). The nucleation time (the time at which a peak starts forming), time of maximum crystallization rate (the time of peak maximum), and heat of crystallization (proportional to peak area) can all be determined from the thermogram. The absence of exotherm recorded by DSC for cream at 4 ° C indicated that no polymorphic reorganizations occurred in milk fat globules during the 30 min after their quenching from 60 ° C (Figure 8.6 ). These studies indicated differences in the polymorphic behavior of TG as a function of their organization, in bulk or dispersed in emulsion.

Figure 8.5. Crystallization properties of anhydrous milk fat. Left: Three - dimensional plots of the XRD patterns recorded at small and wide (insert) angle during cooling from 60 ° C to − 7 ° C at 1 ° C.min − 1 . Right: Evolution of the maximal intensity of the XRD peaks recorded at small angles, allowing relation of the structural data to the thermal properties recorded simultaneously by DSC.


DSC and XRD investigations showed that the fat phase of dairy products displays a complex polymorphism. Depending on the cooling rate, six different types of crystals were identifi ed, several of them in coexistence, and their time - and temperature - dependent evolutions were quantitatively monitored. They correspond to lamellar structures with 2L (40.5 – 48 Å ) and 3L (54 – 72 Å ) organizations of TG. At least fi ve crystalline subcell species were observed at wide angles: α and sub - α , two β ′ , and one β . All these crystalline structures coexist with a liquid phase even at low temperature ( T < 4 ° C). Thermal events recorded by DSC were related to the structural information on the organization of TG obtained by XRD. These experiments focusing on the crystallization properties of whole milk fat and fat fractions char-acterized by different composition and thermal properties contributed to the development of spreadable butters with defi ned solid fat content as a function of temperature.

Figure 8.6. Left: Three - dimensional plot of the isothermal evolution of small - angle and wide - angle (insert) XRD patterns recorded at 4 ° C after rapid quenching from 60 ° C of anhydrous milk fat (AMF). Upper left: Time evolution of the intensities, taken at the peak maximum and normalized to 100%, of the XRD patterns recorded at small angles; DSC recordings of AMF and cream obtained simultaneously with XRD experiments.


Polymorphism of TG in milk fat globules In milk, lipids are naturally dispersed as small droplets (4 μ m) called the milk fat globules . Studying the crystallization of TG in milk fat globules is of prime importance because it affects many properties, such as (1) rheological properties, (2) resistance of fat globules to disruption and then to coalescence, (3) susceptibility of globules to churning for the manufacture of butter, (4) stability of whipped cream, and (5) consistency and mouth feel of high - fat products. Thus, it is important to understand better the physical properties of fat globules, for example, their thermal and crystallographic properties, for indus-trial applications and to improve the quality of food products.

Moreover, it is interesting to compare crystallization of fat dispersed in an emulsion such as milk or cream (which is the concentration of fat globules from milk) in which fat globules are surrounded by a membrane rich in phospholipids with crystallization of bulk milk fat. Lopez et al. (2002a,b) showed that the dispersion state of milk fat, for example in bulk as anhydrous milk fat or dispersed in fat globules, alters both its thermal and structural properties. The use of DSC coupled to synchrotron radiation XRD allowed identifi cation of the crystalline structures formed by TG molecules as a function of tem-perature and time in dispersed systems such as milk fat globules (Lopez et al. 2000, 2001c, 2002a, b ). Figure 8.7 shows that slow cooling of cream (0.15 ° C.min − 1 ) leads to the recording of a single exotherm cor-responding to crystallization of TG in fat globules. The organization of TG molecules in the solid state (e.g., in fat crystals) investigated using XRDT allowed the identifi cation of four crystalline structures that are successively formed as a function of the decrease in tempera-ture (Lopez et al. 2001c ).

Studies on milk fat globules showed that the temperature of the beginning of crystallization is lowered as a function of the decrease of their size (Lopez et al. 2002b ; Michalski et al. 2004 ). XRD permitted the identifi cation of different crystallization behavior in natural milk fat globules with different sizes, which could be implicated in the manufacture of dairy products involving tempering periods in the tech-nological process (butter, ice cream, whipped products).

The examination of TG polymorphism in milk fat globules is much more challenging than for fat in bulk and especially diffi cult because (1) both small - and wide - angle XRD should be considered at the same time and compared to determine the evolution of each of the species


as a function of time; (2) the X - ray intensity diffracted by each of the crystalline structures is proportional to the fraction of particular crystal in the structure; (3) the whole XRD signal is largely absorbed by the surrounding water and its solutes (e.g., casein micelles, minerals, lactose); and (4) the peak broadening results from the crystallization constraints in dispersed systems and the smaller size of the crystals.

Crystallization properties of fat in dairy products The crystallographic and thermal properties of fat in complex food products have also been investigated. The melting properties of butters with different fatty acid composition showed different DSC profi les, which have been related to the textural properties of the butters (Lopez at al. 2007). Lopez et al. (2008) identifi ed the crystalline structures formed by TG in Emmental cheese at 4 ° C and their melting behavior upon heating. Recently, DSC was used to investigate the thermal prop-erties of fat in cheese. Lopez et al. (2006a) showed that the liquid - to - solid phase transition recorded by DSC upon cooling is sensitive to the

Small-angle XRD DSC Wide-angle XRD

b’

a

3L1(001)

3L1(002)

3L2(002)

0.15°C/min

Liquid

3L1(003) 3L1(005)

12°C 20°C

22°C

0.1 0.2 0.3q (Å–1)

T (

°C)

0.4 0.5 1.1 1.3 1.5q (Å–1)

1.7

–5

0 10 20Temperature (°C)

30

6

16

27

37

47

71.3Å

2L2

En

do

> (

u.a

.)

40Å

2L1

46.5Å

3L2

65Å

T (

°C)

–4

6

16

26

36

Figure 8.7. Structural evolution, expressed in scattering vector q ( Å − 1 ), of TGs dis-persed in milk fat globules during cooling from 60 ° C to − 8 ° C at 0.15 ° C.min − 1 . Three - dimensional plots of the XRD patterns recorded at small angles (left) and at wide angles (right). DSC curve recorded simultaneously.


destabilization of fat globules and the formation of nonemulsifi ed fat during the manufacture of Emmental cheese. Moreover, these authors developed a protocol to determine the solid fat content in cheese at 4 ° C and the evolution of the ratio of solid to liquid fat as a function of temperature (Lopez et al. 2006a ).

Lard

Pork production is estimated at about 93 × 10 6 tons, of which approxi-mately 50% and 22% are produced in China and Europe, respectively. Pork represents almost 40% of worldwide daily meat protein intake. Lard is the fat obtained by rendering fatty tissue of the hog, the domes-tic pig. Natural lard has a characteristic waxy texture and exhibits unsatisfying bakery qualities that are frequently corrected by fat blend-ing, partial hydrogenation, or interesterifi cation in making commercial shortenings. The composition of lard varies with the hog ’ s food and is mainly composed of a few long - chain major fatty acids, including C16:0 ( ∼ 24%), C18:0 ( ∼ 14%), C18:1 ( ∼ 41%), and C18:2 ( ∼ 10%).

Although composed of only a few TGs, lard, as many other fats, exhibits complex thermal properties. DSC thermograms of lard exhibit several peaks upon heating or cooling of samples (Figure 8.8 ). These peaks refl ect the occurrence of numerous thermal transitions, the tem-peratures and enthalpies of which vary as a function of sample thermal history. The underlying polymorphic transitions of DSC peaks have been identifi ed (Table 8.3 ) and shall be illustrated for fast cooling and heating rates at 5 ° C/min.

For each of the thermal events recorded upon crystallization, a dis-tinct structure of TG molecules is evident (Figure 8.9 , top). Even at fast cooling rates, the c 1 thermal event is associated with the formation of a 2L α form, whereas c 2 and c 3 can be attributed to two 2L β ′ struc-tures from the evolution of the peak intensities as a function of tem-perature (Figure 8.9 , bottom). It is important to point out that that c 1 crystallization did not occur at cooling rates lower than 2 ° C/min. In addition, at lower cooling rates the structure formed during c 2 exo-therms crystallizes in a 2L β structure (Kalnin et al. 2005 ). Upon heating, more than seven endotherms have been observed and attrib-uted to the polymorphic forms (Figure 8.9 ) by using MICROCALIX (Kalnin et al. 2005 ). Two main melting endotherms, the temperature positions of which vary widely, are observed around 0 ° and 30 ° C.

Norm

aliz

ed H

eat F

low

(W

/g)

→

5.0 e4 e3

c3 c2 c1

Tonset (c1)

cooling rate (rc) in K/min

cooling

0.2 (× 12)

0.5 (× 5)

1 (× 2.5)

2 (× 2.5)

5 (× 2)

10

Tonset (c2)Tonset (c3)

endo

e2

4.0

3.0

2.0

1.0

0.0

–1.0–40 –20 0

Temperature (˚C)

20 40 60

Figure 8.8. Characteristic DSC curves of the crystallization of lard. The infl uence of the cooling rate ( r c ) is evidenced in the range of − 0.2 up to − 10 K/min as indicated. The normalized heat fl ow is scaled to the cooling rate of − 10 K/min. DSC curves are shifted relatively to each other and multiplied for clarity as indicated in brackets. When not associated to T onset , arrows indicate minor exothermic events.

Table 8.3. Main crystallographic parameters of the fat crystals in lard and their attribution to major DSC peaks.

Crystalline Form/Subcell

SAXS Peaks d ( Å )

WAXS Peaks d ( Å )

DSC Exotherms

DSC Endotherms

α (hexagonal) 48.2 4.18 c1 h3 β ′ 1

(orthorhombic) 35.1 4.14, 3.83 c2 h4, h5

β ′ 2 (pseudo - orthorhombic

43.8 3.95, 4.4, and 4.3

c3 h7, h6

β triclinic) 43.8 4.6 c2 (only upon slow cooling rates)

h1, h2

191

500

400

300

U-shaped 43.6Å

35.1Å

48.2Å

c2c3

c3 β’ = 3.79Å

β’ = 3.83Å

c3

c2

c2c1

Heatingat rH = 5 K/min

40.0°C

34.6°C

29.2°C

18.9°C

8.4°C 4.14Å

3.95Å

4.18Å

–20.5°C

–15.9°C

–12.7°C–4.8°C8.9°C

13.9°C19.2°C

Crystallizationat rC = 5 K/minc2

c1

l (c

ps)

200

100

00.05 0.10 0.15 0.20

q (Å–1) q (Å–1)

0.25 0.30 1.10 1.20 1.30 1.40 1.50 1.60 1.70 1.80

60

40h2 + h1

h3h4

h5

h6

h7

c3

c2

endo

rc = –5 K/min

rH = –5 K/min

c1

20

0

–20

0

20

40

60

–6 0

Heat Flow (mW) Relative Peak Intensity (%)

6 1.0 0.5 0.0 1.0 0.5 0.0

40.0°C

34.4°C

29.2°C

18.9°C

8.4°C

–20.5°CWAXS

DSC SAXS WAXS

–12.7°C

–4.8°C

8.9°C

13.9°C

19.2°C

Tem

pera

ture

(°C

)

48.2 Å43.8 Å35.1 Å

4.15 Å3.8 Å3.95.1 Å + 4.3 Å + 4.4 Å

Figure 8.9. A selection of small and wide angle X - ray scattering (SAXS) patterns at the average temperature indicated is redrawn on top to illustrate crystallographic properties of lard. Pure tristearin (SSS) WAXS pattern is shown for comparison (dashed line). All X - ray patterns were recorded for 60 s and shifted relatively to each other for clarity. The corresponding DSC curves are drawn for comparison next to SAXS and WAXS relative peak intensity plots vs. T . Evolution of three SAXS and fi ve WAXS lines were followed and plotted with normalizations on the peak maximum intensity (fi gure redrawn from Kalnin et al. 2005 ). Main crystallographic parameter of the crystals in lard and their attribution to major DSC peaks (right).

192


They could be attributed to the mono - and di - unsaturated fractions crystallizing in 2L β ′ forms according to the variation of the peak intensities as a function of temperature (Figure 8.9 , bottom). The two overlapping endotherms, h1 and h2, the importance of which increases with decreasing cooling rate, are only recorded at T > 40 ° C and show that transition of 2L α 2L β ′ form. The observed transition at high temperatures, however, is dependent on the whole thermal history. Thus, h1 is only observed at low cooling rates or after a longer time.

It can be concluded from this study that, in general, saturated, mono-saturated, and polyunsaturated TGs do not cocrystallize at all cooling rates, but intersolubility is temperature and time dependent. The phase separation between layers of saturated - saturated - saturated TG, satu-rated - saturated - oleic acid TG, and saturated - oleic acid - saturated TG was supposed to lead to an alternate structure due to oleic acid este-rifi ed in sn - 2 and sn - 3 positions, which might explain the diffraction patterns for the observed pseudo - orthorhombic structure (Kalnin et al. 2005 ). Moreover, structural transitions take place after rapid cry-stallizations even at very low temperatures due to α to β ′ transition based on isothermal recordings using MICROCALIX (Kalnin et al. 2005 ).

Coupling of DSC and XRDT using MICROCALIX allows the partial identifi cation of the structures developed during the thermal treatments, both fast and slow rates, and permits the assignment of the thermal events recorded by DSC. Once this identifi cation has been established, conventional not “ coupled ” DSC analysis can be under-taken using commercially available DSC. This knowledge is further used for fractionation of lard.

Conclusion

Elucidating the polymorphism of lipids is important to better under-stand the properties of fat - rich food products and to improve food technology.

The presence of one or several long fatty acid chains and their dif-ferent packings lead to a variety of polymorphic forms that cause the thermal and structural behaviors of lipids to be rather complex to study. Both types of properties are currently measured by XRD and DSC


alone. The thermal and structural characterizations of fats are generally obtained on an independent apparatus, which does not facilitate the correlation between both types of phenomena. The new instrument, MICROCALIX, allowing simultaneous (time - resolved synchrotron) XRD at both wide and small angles as a function of temperature (XRDT) or time (XRDt), coupled with high - sensitivity DSC, permits the study of lipid properties for food applications. The technique has demonstrated the power of coupling for the investigation of the proper-ties and structures of lipids. MICROCALIX has also shown its useful-ness in domains such as chemistry and biology for cosmetic and pharmaceutical applications.

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Rowney M. , Roupas P. , Hickey M. , and Everett D.W. 1998 . Milkfat structure and free oil in Mozzarella cheese . Aust J Dairy Technol , 53 : 110 .

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Sato K. , Arishima T. , Wang Z.H. , Ojima K. , Sagi N. , and Mori H. 1989 . Polymorphism of POP and SOS. I. Occurence and polymorphic transformation . J Am Oil Chem Soc , 66 : 664 – 674 .

Small D.M. 1986 . In: Handbook of lipid research. The physical chemistry of lipids. From alkanes to phospholipids. Plenum Press: New York.


ten Grotenhuis E. , van Aken G.A. , van Malssen K.F. , and Schenck H. 1999 . Polymorphism of milk fat studied by differential scanning calorimetry and real - time x - ray powder diffraction . J Am Oil Chem Soc , 76 : 1031 – 1039 .

Timms R.E. 1980 . The phase behavior and polymorphism of milk fat, milk fat frac-tions, and fully hardened milk fat . Aust J Dairy Technol , 35 : 47 – 53 .

Timms R.E. 1984 . Phase behavior of fats and their mixtures . Prog Lipid Res , 23 : 1 – 38 .

van den Tempel M. , 1979 . Crystallization in dispersed systems . In: Physico - chimie des compose é s amphiphiles , R. Perron R. , P. Bothorel P. , editors, pp. 261 – 264 . Colloques nationaux du C.N.R.S . n 938.

van Malssen K.F. 1994 . Real - time x - ray powder diffraction applied to cocoa butter and graphite intercalates . Ph.D., Amsterdam University, The Netherlands.

Wille R.L. and Lutton E.S. 1966 . Polymorphism of cocoa butter . J Am Oil Chem Soc , 43 : 4914 – 496 .

Calorimetry as a Tool for Process Design

Part 2

Chapter 9

Overview of Calorimetry as a Tool for Effi cient and Safe Food - Processing Design

Alois Raemy , Corinne Appolonia Nouzille , Pierre Lambelet , and Alejandro Marabi

201

Introduction 202 Generalities About Thermal Analysis and Calorimetry 203

Techniques 203 Methods 205 Samples 206

Thermal Behavior of Food Constituents 206 Carbohydrates (sugars) 206 Lipids 208 Proteins 214 Water 216

Thermal behavior of Raw and Reconstituted Food 217 Safety Aspects 217 Other Thermodynamic Parameters 218

Heat of Solution 218 Specifi c Heat 224 Heat of Combustion 225

Related Techniques 225 Interest of Calorimetry for the Food Industry 226 Conclusion 226 References 227


Introduction

In the investigation of foods using thermal analysis and calorimetric techniques, many physicochemical effects can be observed in the tem-perature range between − 50 ° C and 300 ° C. These thermal phenomena may be either endothermic, such as melting, denaturation, and vapor-ization, or exothermic, such as crystallization, oxidation, and fermenta-tion. Some exothermic reactions present a hazard in industrial operations or during storage. They can lead either to self - ignition, causing fi res or dust explosions in open systems such as spray - dryers, or to pressure increase and bursting in closed vessels such as auto-claves or extraction cells. Glass transitions are observed as a shift in the baseline; this information, associated with humidity and water activity determination, is of particular interest in relation to storage of food powders, but also for gas retention in powders predicted to foam when dissolved. This is the safe - processing aspect of thermal analysis and calorimetry.

The thermal behavior of food strongly depends on its composition. We therefore consider primarily thermal characteristics of the major food constituents: carbohydrates, lipids, proteins, and water. Specifi c thermal phenomena of minor constituents (e.g., caffeine), as well as those of additives such as emulsifi ers, are mentioned. Raw and recon-stituted foods and fi nally interactions between food constituents will be considered. Some of these aspects will only be mentioned and not fully discussed. The reader should refer to previous work by the same authors (Raemy and Lambelet 1991 ; Raemy et al. 2000, 2004 ).

Emulsifi ers (endogenous or exogenous) are often used in the food industry to stabilize interfaces in emulsions and foams. When added to an aqueous phase, emulsifi ers often spontaneously form self - assem-bly structures. Such structured fl uids can be used as active ingredients for encapsulation or as microreactors for fl avor formation. In this context, differential scanning calorimetry (DSC) instruments, espe-cially micro - DSC, can help detect liquid crystal phase transitions and establish phase diagrams. This is the material science aspect of thermal analysis and calorimetry.

Other thermodynamic parameters, such as heat of solution, specifi c heat, and heat of combustion can be determined; they are also impor-tant for effi cient food - processing design. Here, we focus on heat of a solution that is of great interest for food powder dissolution.

Overview of Calorimetry as a Tool 203

Some related or complementary techniques also are mentioned to situate thermal analysis and calorimetry in the physicochemistry domain.

Generalities About Thermal Analysis and Calorimetry

Thermal analysis and calorimetry are extensively described in the lit-erature (Miller 1982 ; Hemminger and H ö hne 1984 ; Sestak 1984 ; Widmann and Riesen 1987 ; Hemminger and Cammenga 1989 ; Haines 2002 ; Claudy 2005 ). See also Chapter 1 .

Techniques

The most currently used technique today is DSC, which often replaces the older differential thermal analysis (DTA). DSC instruments are classifi ed into power - compensated DSC instruments (Perkin - Elmer instruments) and heat fl ow calorimeters. Heat fl ow calorimeters can in turn be classifi ed into Calvet - type calorimeters, where the thermopiles surround the sample and reference cells (Setaram Instruments, Caluire, France) and those where the thermopiles are below the sample and reference crucibles (suppliers being Mettler - Toledo AG, Schwerzenbach, Switzerland; Netzsch - Ger ä tebau GmbH, Selb, Germany; TA Instru-ments, New Castle, DE). For isothermal dissolution measurements a Calvet - type calorimeter equipped with a specially designed membrane cell can be used as shown in Figure 9.1 .

The liquid and the powder sample are placed in the measuring cell separated by a membrane. The same amount of liquid is also placed in the reference cell. The cells are then introduced in the calorimeter, and thermal stabilization is achieved after some minutes. The measure-ment is then initiated, and after checking for a stable baseline, the membrane is pierced and mixing is started, bringing the solid and the liquid into close contact. The heat released or absorbed is measured in relation to the reference cell. A calorimetric curve is obtained, and the area under the curve is automatically integrated, yielding the heat (J/g) absorbed or released during dissolution of the solid sample. A negative value for the enthalpy of dissolution indicates the emission of heat (exothermic process), and positive values indicate an absorption of heat (endothermic process).


Other calorimeters are used for specifi c applications, for example, adiabatic calorimeters and the accelerating rate calorimeters, or ARC (Thermal Hazard Technology, Piscataway, NJ). These are particularly useful for process safety as adiabatic conditions are the most dangerous thermal conditions for a product.

The more recent technique of modulated DSC (MDSC) or alternat-ing DSC (ADSC) is that in which a modulated temperature signal is superimposed on the temperature ramp to help separate reversible phenomena (e.g., glass transition) from nonreversible phenomena (e.g., relaxation).

Adiabatic or isoperibolic bomb calorimeters are used to determine the heat of combustion of foods. Although the values may be important in the context of process safety, they are mainly used to calculate the caloric value of food for human nutrition or when foods are used as energy sources (e.g., bio - ethanol) for engines.

Some thermal analysis instruments or microcalorimeters allow either working under virtually fi xed pressures (a high - pressure auto-clave surrounds the cells) or give thermomanometric information (the cells are linked to a high - pressure system or even directly fi tted with pressure sensors). Even if thermomanometric information is rarely given in the literature, these techniques are of great interest for process

Mixing rod

a b c

Liquid reservoir

Aluminummembrane

Recipient for solid

sample

Figure 9.1. Instrumentation used for performing heat of solution measurements (Calorimeter Setaram C80 and cell with membrane): (a) general view, (b) the heating block showing the geometry for the reference and measurement cells, and (c) the membrane mixing cell. The solid sample and the liquid are effi ciently separated during the thermal stabilization step, without risk of moisture transfer. From Marabi et al. (2007b) . Courtesy of Setaram .


safety applications, as increased pressure is responsible for bursting of autoclaves.

For high - sensitivity measurements, various microcalorimeters, working only in isothermal mode (Thermometric AB, Jarfalla,Sweden), or micro - DSC, working in isothermal and scanning mode (Setaram Instruments) are available today. Another way to increase sensitivity is to use high heating rates: 10 ° C/min with a standard DSC instrument or up to 500 ° C/min with new DSC instruments (sometimes called hyperDSC).

For high - resolution measurements, best results are obtained with small samples and slow heating (cooling) rates. Resolution of DSC instruments can be checked with the help of chemical products (Marti et al. 2004 ).

A more extensive classifi cation of the available calorimeters is given elsewhere in the literature (Rouquerol et al. 2007 ).

The criteria for choosing an instrument include temperature range, type of application, ability to work under pressure or under gas fl ow, sample size, resolution or sensitivity needed, software performances, and budget.

Methods

DSC measurements can be performed in isothermal or scanning (heating and cooling) mode depending on the instrument and on the goals of the study. The temperature range of the scans has to be decided according to the phenomena of interest. Heating food samples above 100 ° C can lead to pressure increase due to water vaporization; there is therefore a risk of cell rupture if sealed cells are used. Cooling food below 0 ° C also can provoke a cell rupture due to volume expansion upon crystallization.

Generally, to obtain clearer interpretation of the thermal transitions, consecutive scans (generally fi rst and second scans) are performed; they allow identifi cation of which phenomena are reversible and which are not. Sequences of heating - cooling - heating scans are often helpful.

Concerning heating rates, the specialist will generally select the value giving the best possible curves (often 5 ° C/min with a DSC and 0.5 ° C/min with a micro - DSC instrument). However, to use (Arrhenius type) kinetic models to fi t the curves, measurements at different heating rates are sometimes required (Roduit 2002 ). The ARC uses a special


heat - search procedure to fi nd exothermic phenomena (Raemy and Ottaway 1991 ).

Calibration is checked with metals (e.g., In, Sn, Pb) or chemicals (naphthalene, which has been contested due to health issues; benzoic acid). With Calvet - type calorimeters Joule effect measurements can also be performed with specially designed cells.

Samples

Generally, food products are available in large quantities and are easy to handle. The size of the samples to study is very important. The samples must be representative of the food of interest; if it is a lipid, a very thin layer (2 – 5 mg) is suffi cient, so most DSC instruments can be used. If the samples are beans (e.g., coffee, cocoa, cereals), large cells are required, thus there is a need for special instruments allowing study of some hundreds of milligrams or even grams.

When small sample sizes are used, the reference cell can be empty. When the sample size is larger, the reference cell must be loaded with a material that is inert in the temperature range of interest (generally Al 2 O 3 when studying powders or sometimes water when studying carbohydrate or protein solutions). Reference and sample cells are thus equilibrated.

Thermal Behavior of Food Constituents

Foods are mainly composed of carbohydrates, lipids, proteins, and water. In addition, they contain small proportions of minerals and various organic substances. Minerals are often analyzed globally as ash. The organic substances can be vitamins, caffeine, emulsifi ers, acids, antioxidants, pigments, polyphenols, or fl avors. Before present-ing the thermal behavior of raw and reconstituted foods, we fi rst describe the thermal behavior of the main food constituents.

Carbohydrates (Sugars)

The main phenomena observed during heating of carbohydrates are release of crystallization water, melting, decomposition, gelatinization of starch in the presence of water, and retrogradation of the gel. In


addition, glass transition, relaxation, and crystallization of amorphous samples occur (Raemy and Schweizer 1983 ; Blanchard and Lillford 1993 ; Raemy et al. 1993 ; Roos 1995 ; Vuataz 2002 ). Glass transition indicates that amorphous carbohydrates change from the glassy state to the rubbery state during heating; glass transition is often superim-posed on the relaxation phenomenon. A glass transition is reversible and observed as a change in baseline, whereas relaxation is a nonre-versible endothermic transition.

As shown in Figure 9.2 , the glass transition (superimposed with relaxation in the fi rst scan) temperature and the crystallization tempera-ture diminish rapidly with increasing water activity. Amorphism, even at low levels (down to about 0.5%), can be detected and quantitatively determined on the basis of the crystallization enthalpies (Raemy et al. 1993 ), sometimes from the height of the glass transition.

In Figure 9.3 , the phenomena observed for these crystalline carbo-hydrate samples are melting followed by decomposition. Tables with melting and decomposition temperatures of carbohydrates as well as corresponding enthalpies are given in the literature (Raemy and Schweizer 1983 ).

Gelatinization of starch - water systems is an endothermic nonrevers-ible phenomenon easily observed by DSC. Retrogradation, which is a slow and low - energy recrystallization process, can be followed by isothermal microcalorimetry (Raemy et al. 1990 ; Silverio et al. 1996 )

55Exo↑

50

45

40

35

30

25

20

15

10

520

Aw 0.078

dQ

/dt [m

W]

Aw 0.112

Aw 0.176

Aw 0.225

40 60Temperature [°C]

80 100

Figure 9.2. Calorimetric curves of amorphous sucrose at increasing water activities. Setaram Micro - DSC III, 1 ° C/min. From Raemy et al. (1993) , with permission.


but is more often characterized after a storage period by measuring the melting transition of the retrograded gel.

Lipids

Oils and fats reveal many thermally induced transitions as a result of heating or cooling. These transitions are fundamental and can be used to elucidate chemical and physical properties of oils and fats.

Thermal analysis and calorimetric techniques (mainly DTA, DSC) have been methods of choice for studying thermal transitions in bulk lipids for more than 60 years. They have been proven to be the most effi cient techniques for studying thermal effects that occur during melting, crystallization, and oxidation of lipids. More recently, DSC measurements have also been applied for investigating properties of lipids in dispersed systems; thus, numerous recent calorimetric studies concern membrane lipids (phospholipids and glycolipids). Interactions

50 100 150

Cellobiose

Sucrose

Galactose

d

d

d

f

f

f

Heat flow

, J/s

1°C/min

Exo

200 250

Temperature, °C

Figure 9.3. Calorimetric curves of crystalline galactose, sucrose, and cellobiose, all three heated in sealed cells up to 260 ° C. Calorimeter Setaram C80, 1 ° C/min. From Raemy and Schweizer (1983) , with permission.


between lipids and other macronutrients are also a subject of recent calorimetric investigations.

Bulk s ystems Melting p rofi le DSC melting curves give valuable information on the melting profi le of triacylglycerols (TAGs) and fats, for example, how they melt in the mouth. The complexity of thermal profi les of oils and fats is essentially due to their great variety of TAGs. Calorimetry has long been used to determine the melting profi les of TAGs and fats for controlling technological processes such as blending (Bartsch et al. 1990 ), chemical or enzymatic interesterifi cation (Dian et al. 2006 ; Vu et al. 2007 ), fractionation (Herrera and Anon 1991 ; Bhaskar et al. 1998 ), and hydrogenation (Daniels et al. 2006 ). Changes in melting temperature and enthalpy also have been correlated to fat composition (Tan and Man 2000 ).

Solid fat content (SFC), which represents the ratio of solid to liquid in a partially crystallized lipid at a given temperature, can be obtained from the calorimetric melting curve by sequential peak integration (Lambelet et al. 1986 ; Kaisersberger 1989 ; Bhaskar et al. 1998 ). SFC values are currently used in the fat industry for quality control. Accurate determinations of SFC values require, however, knowing the exact melting enthalpy of each phase or of the various fractions present in a sample, which is very diffi cult to assess for most fats.

Polymorphism Polymorphism of TAGs and fats as well as phase transitions between the various polymorphic forms have been exten-sively studied by calorimetry (Wille and Lutton 1966 ; Huyghebaert and Hendrickx 1971 ; Dimick and Manning 1987 ; Garti and Sato 1988 ; Arishima et al. 1991 ; Loisel et al. 1998 ; Lovegren et al. 1976 ; Merken and Vaeck 1980 ; Minato et al. 1997 ; Rousset 1997 ; Rousset and Rappaz 1996 ; Sato 1996 ; Spigno et al. 2001 ). These studies have been conducted by measuring the melting enthalpy and temperature (pure components) or temperature range (complex mixtures such as fats) of the phases present in a lipid sample, as shown for cocoa butter in Figure 9.4 .

For binary or ternary mixtures of TAGs or fats, DSC has been used to determine real or “ pseudo ” phase diagrams, or iso - solid diagrams, by identifying the domains of the various phases formed (Knoester


1972 ; Lambelet and Raemy 1983 ; Ali and Dimick 1994 ; Culot 1994 ; Elisabettini et al. 1998 ; Koyano et al. 1992 ; Rousset et al 1998 ; Timms 1994 ).

Kinetics of c rystallization Crystallization kinetics of bulk lipids and the formation and stability of their various polymorphs as a function of time and temperature are another domain in which DSC is very useful. For these measurements, the lipid sample has fi rst to be heated to at least 20 ° C above the melting temperature of its stable polymorph to erase all memory effects. Kinetic information has been obtained by measuring either isothermally after quenching at the desired tempera-ture (Rousset and Rappaz 1997 ; Metin and Hartel 1998 ; Toro - Vazquez et al. 2005 ) or at constant cooling rate under various cooling conditions (Kawamura 1980 ; Cebula and Smith 1991 ). Complex thermal paths such as tempering stages also were studied by calorimetry to under-stand precisely the mechanisms that induce the appearance of stable crystalline forms (Rousset and Rappaz 1997 ).

Precise kinetic parameters can be determined from isothermal experiments. The variation of SFC as a function of time can be obtained by sequential integration of the crystallization peak. This SFC function is then used to estimate crystallization parameters with the help of the Avrami or more complex models (Kloek et al. 2000 ; Foubert et al. 2002 ; Rousset 2002 ). Nucleation induction times, which are periods

30 35 40 4525

dH

/dt (W

/g)

II III IV

VMelting curvesExo↓ VI

3.5

3

2.5

2

1.5

1

0.5

02015

T (°C)

Figure 9.4. DSC heating curves of fi ve polymorphs of cocoa butter. Mettler FP900, 5 ° C/min. From Rousset (1997) .


of time before nucleation appears, also can be determined from iso-thermal crystallization experiments (Rousset and Rappaz 2001 ). These kinetic parameters are useful indicators of the nucleation rate and may serve as a basis for predicting how to crystallize lipids in the desired form.

Kinetic studies also help to understand the effects of compositional changes (TAGs or other minor components) on crystallization (Garti et al. 1988 ; Wahnelt et al. 1991 ; Tan et al. 2000 ; Vanhoutte et al. 2002a,b ). However, as samples are not mixed, results from DSC crys-tallization studies are often diffi cult to interpret directly in terms of process operating conditions (Rousset and Rappaz 2001 ; Hartel 2001 ).

DSC curves are often complex because various modes of crystalliza-tion and solid state transformations can contribute to a single peak. To defi ne unambiguously the key signal characteristics such as minima and maxima, as well as end and start points, a method based on the determination of the fi rst and the second derivative of the DSC raw signal has been proposed (Bouzidi et al. 2005 ).

Quality c ontrol DSC crystallization and melting profi les of lipids have been used to assess the quality of oils, in particular of heated oils (Gloria and Aguilera 1998 ; Tan and Man 1999, 2000, 2002 ). Similarly, contamination (adulteration) of fats and fat - based products can be detected by calorimetry (Lambelet and Ganguli 1983 ; Bringer et al. 1991 ; Marikkar et al. 2002 ). Adulteration was demonstrated by the appearance or a modifi cation (shift in the peak position and peak area) of a thermal transition occuring in the heating or cooling DSC curves of lipid mixtures.

Oxidative s tability of o ils and f ats Lipid oxidation is an exothermic phenomenon that can be observed by continuous monitoring of total thermal effect either under isothermal or nonisothermal conditions of measurements (Raemy et al. 1987 ; Kowalski 1989 ; Tan and Man 2002 ; Ulkowski et al. 2005 ). Measurements can be performed under a static air atmosphere or, preferably, under oxygen fl ow or oxygen pressure (Litwinienko and Kasprzycka - Guttman 1998 ).

In isothermal experiments, oxidation induction times correspond to the time at which a rapid exothermic reaction between the lipid and oxygen occurred. Tables of oxidation induction times measured by isothermal heat fl ux calorimetry around 100 ° C are reported in the


literature (Raemy et al. 1987 ). For edible oils, induction times obtained by calorimetry were shown to correlate well with corresponding values determined by traditional methods (Tan et al. 2002 ). DSC can therefore be used to assess the oxidative stability of lipids (Raemy et al. 1987 ; Kowalski 1989 ; Tan and Man 2002 ).

In the nonisothermal mode, Arrhenius kinetic parameters can be deduced from the shape of the oxidation curves, and these parameters can in turn be applied for calculation of the overall fi rst - order rate constant of oxidation at various temperatures (Litwinienko and Kasprzycka - Guttman 1998 ; Ulkowski et al. 2005 ).

Antioxidant e ffi cacy Food antioxidant activity can be measured by calorimetry. The effi ciency of an antioxidant to protect an oil is mea-sured by the increase of induction time after incorporation of the test antioxidant into the oil. (Raemy et al. 1987 ; Irwandi et al. 2000 ; Tan et al. 2002 ; Giuffrida et al. 2006 ). A good correlation between DSC oxidative induction time and oxidative stability index determined by other analytical techniques was found (Tan et al. 2002 ; Gouveia et al. 2006 ; Giuffrida et al. 2006 ).

Also, the radical scavenging activities of antioxidants can be inves-tigated by DSC monitoring of the polymerization of substrates initiated by radical reactions (Fujisawa and Kadoma 2006 ).

Dispersed s ystems Emulsifi er - w ater s ystems Lipid - water systems that can be regarded as models of the lipid matrix of cell membranes include emulsifi ers that may exhibit highly ordered self - assembly structures, which are liquid crystalline phases. DSC has been applied to the study of endo-thermic phase transitions occurring in lipid - water systems (Blume 1991 ;

Figure 9.5. (a) Presentation of the calorimetric curve of a saturated MAG with 20% water showing melting of different crystalline forms up to 70 ° C and weak liquid crystal transitions at 85 ° C and 110 ° C. (b) Presentation of the following cooling curve, which shows that there is practically no hysteresis between the temperatures of the phenomena; however, the crystalline form melting at 45 ° C has disappeared. (c) Presentation of the second heating curve, which confi rms the reversible character of most transitions. (a – c) Setaram Micro - DSC III, 0.2 ° C/min. From Raemy et al. (2005) , with permission.

10

7.06.56.05.55.04.54.03.53.02.52.01.51.00.50.0

Heat flow

/mW

Exo →

liquid crystal

phase transitions

20 30 40 50

crystallization

60 70 80 90 100Temperature (°C)

10 20 30

0.5

(a)

(b)

(c)

–0.5

–1.5

–2.5

–3.5

–4.5

–5.5

–6.5

–7.5

40

meltingH

eat flow

/mW

Exo →

liquid crystalphase transitions

50 60 70 80 90100

Temperature (°C)

10 20 30

–0.5

–1.5

–2.5

–3.5

–4.5

–5.5

–6.5

–7.5

40

melting

Heat flow

/mW

Exo →

liquid crystalphase transitions

50 60 70 80 90100

Temperature (°C)

213


Brandenburg et al. 2006 ) when they transform from the gel to the liquid crystal phase (Chapman et al. 1974 ; T ö lgyesi et al. 1985 ) and to deter-mine thermotropic and lyotropic behavior of these systems (Briggs et al. 1996 ; Qiu and Caffrey 1999 ). Both gel to liquid crystalline β → α acyl chain melting transitions (Mellier 1988 ) and structural transitions between different three - dimensional structures (Willumeit et al. 2005 ) were associated with transitions observed in DSC curves. The most pronounced enthalpy changes were observed for acyl chain melting, whereas structural transitions, which were not accompanied by acyl chain melting, exhibited much lower enthalpy changes.

Micro - DSC can be used at low heating and cooling rates to detect liquid crystalline transitions of exogenous emulsifi er - water systems (Figure 9.5 a, b, and c).

Similarly, DSC has been applied to investigate the thermal behavior of several emulsifi er - water systems modifi ed by changing the pH value, the ionic composition of the environment, or by chemical agents (T ö lgyesi et al. 1985 ; Forte et al. 1998 ; Fournier et al. 1998 ).

Emulsions DSC is useful as it is suffi ciently sensitive to measure transformations in dispersed phases, in particular when used simulta-neously with synchrotron X - ray diffraction (XRD; Kalnin et al. 2002 ). Thermal behavior of lipids in a dispersion or emulsifi ed form has been shown to be quite different from that of the same fat in bulk, for example, crystallization of milk fat (Lopez et al. 2002 ). Lipid poly-morphism in dispersed systems can also be investigated by calorime-try. As shown in Figure 9.6 , polymorphism of colloidal suspensions of TAG has been determined based on DSC melting transitions (Bunjes et al. 2007 ).

Proteins

The main phenomena observed by DSC, and especially micro - DSC, during heating of protein solutions are endothermic phenomena associ-ated with protein denaturation. These phenomena were fi rst described by Privalov (Privalov and Khechinashvili 1974 ). These phenomena were then also observed for products containing large amounts of proteins and enough water to allow protein mobility; for example, for proteins of dairy products where small exotherms associated to protein aggregation also can be detected (Unterhaslberger 2006 ), fi sh and meat

Heating

2 mW

Cooling

S100 / SGC

10 20 30 40 50

Heat flow

Temperature (°C)

60 70 80

α

β

Heating

2 mW

Cooling

S100-3 / SGC

10 20 30 40 50

Heat flow

Temperature (°C)

60 70 80

α

β

Figure 9.6. DSC heating (10 ° C/min) and cooling (5 ° C/min) curves of tristearin nanoparticles stabilized with phospholipid/bile salt blends containing unmodifi ed (top) or hydrogenated soybean lecithin (bottom) shortly after preparation. The dashed arrow in the bottom panel indicates the thermal range of the exothermic event prior to crystallization. Pyris 1 from Perkin Elmer. Endothermic is upward. From Bunjes et al. (2007) , with permission.

215


(Harwalkar and Ma 1990 ), egg (as shown in Figure 9.7 ; Ferreira et al. 1997 ; Grinberg et al. 2002 ), and cereals (Ellepola and Ma 2006 ).

Denaturation can be quantifi ed by the determination of the denatur-ation enthalpy. As the native protein sample is considered to be not denatured at all, its enthalpy in a fi rst scan corresponds to 100% of denaturation. Thermally processed products can then be considered as partially or totally denatured, and a percentage of denaturation can thus be calculated from the determined enthalpies.

The pH is important because it infl uences denaturation temperature and thus allows fi nding protective conditions for industrially treated proteins. Protein denaturation can also be partially or even totally reversible according to the results of second runs.

For dry proteins, glass transition and oxidation also can be observed with thermal analysis techniques.

Water

Crystallization (undercooling) of water, melting of ice, and vaporiza-tion can be observed with thermal analysis and calorimetry. Since the enthalpies corresponding to these phenomena are quite high (333 J/g for ice melting and 2255 J/g for water vaporization), they can be

Denaturation Enthalpy: –1.82 J/g

10 20 30 40 50

Temperature [°C]

Hea

t F

lux [m

W/g

]

60 70 80 90 100

Endo

1

0.5

0

–0.5

–1

–1.5

–2

–2.5

–3

54.9°C91.9°C

Instrument: Micro DSC III SetaramScan rate: 1°C/minSample weight: 0.4152 g

Figure 9.7. DSC curve of the ovalbumin fraction of egg (fi rst minus second run). Setaram Micro - DSC III, 1 ° C/min. From Ferreira et al. (1997) , with permission.


observed easily with standard DSC instruments, even in samples with low water content.

Thermal Behavior of Raw and Reconstituted Food

Most physicochemical effects observed with the main food constitu-ents are also found in the calorimetric curves of raw and reconstituted foods; examples are coffee beans, chicory roots, cereals, milk powders, and infant formulas (Raemy 1981 ; Raemy and Lambelet 1982 ; Raemy and L ö liger 1982 ; Raemy et al. 1983 ; Raemy and Schweizer 1983 ), where carbohydrate decomposition is observed systematically. For milk powders, lactose crystallization and lipid oxidation also are detected. The thermal phenomena observed with pure minor constitu-ents will not be observed, however, once these constituents (e.g., caf-feine) are dispersed in a food matrix. Many raw and reconstituted foods contain water. Therefore, measurements of such products in sealed cells above 100 ° C must only be performed with great precautions because of pressure increase due to water vapor and gas release during decomposition.

In addition to these phenomena, some reactions between food con-stituents, such as the Maillard reactions, which occur between proteins and reducing sugars, may be observed, for example, as an exothermic phenomenon in calorimetric curves of milk powders or infant formulas (Morgan et al. 2005 ) obtained with sealed cells.

Safety Aspects

Carbohydrate decomposition, which sometimes immediately follows melting, lipid oxidation (especially if oil is present as a layer or at the surface of the product) as well as protein oxidation, and even Maillard reactions may present a hazard in industrial operations (e.g., roasting, high - temperature drying).

The role of thermal analysis and calorimetry for determining safe conditions of industrial processes has already been explained else-where (Raemy and L ö liger 1985 ; Raemy et al. 1985 ; Raemy and Gardiol 1987 ; Raemy 1988, 2001 ). The application of adiabatic calorimetry to the study of cellulose decomposition, cellulose being


considered as a model for other foods, has been described in detail (Raemy and Ottaway 1991 ). Pressure increase due to water vapor pres-sure, gases evolved during roasting or decomposition, and air compres-sion (the pressure increase due to dilatation of the pressure sensor has to be deduced) can be monitored by thermomanometry, for example, with the C80 calorimeter or with the ARC.

In the case of safety studies, thermal analysis and calorimetric tech-niques must sometimes be applied unconventionally as measurements have to be carried out under conditions close to those of the process to be studied (Raemy 1992 ; Raemy et al. 2000 ).

Other Thermodynamic Parameters

In addition, to observe the thermal behavior of food as a function of temperature, calorimetric techniques can also be used to determine thermal parameters such as heats of solutions, specifi c heats, and heats of combustion.

Heat of Solution

Solution calorimetry is a suitable technique for the study of liquid - liquid and liquid - solid interactions (Hogan and Buckton 2000 ). It allows quantifying the thermodynamic effects that occur during the dissolution process and can potentially give information on the kinetics and the mechanism of dissolution. In a calorimetric experiment address-ing the dissolution process, the output is a composite of wetting, liquid penetration, dissolution phenomena (disruption of the solid, removal of surface molecules, and incorporation of the solute molecules in cavities in the solvent), and any other interactions that might occur (Buckton 1995 ; Gao and Rytting 2006 ).

This is a widely used technique, mostly in the pharmaceutical fi eld. More specifi cally, solution calorimetry is used for quantifying the heat as a solid dissolves in a liquid. The theory behind the technique has been explained in detail previously (Gao and Rytting 2006 ).

Some examples include the determination of amorphous content of lactose (Hogan and Buckton 2000 ; Harjunen et al. 2004 ; Dilworth et al. 2004 ; Katainen et al. 2005 ), sucrose and drugs (Gao and Rytting 2006 ), the enthalpy of solution of α - cyclodextrin (Bastos et al. 2004 ),


various saccharides (Miller and de Pablo 2000 ), and caffeine (Pinto and Diogo 2006 ), to name a few. The swelling of seaweed and green tea has been studied by means of calorimetry (Miyagawa et al. 1995a,b ), and the swelling and dissolution behavior of different polymers and polymer blends used as pharmaceutical excipients have also been reported (Conti et al. 2006 ).

The signifi cantly different dissolution behavior of crystalline and amorphous saccharides has also been studied (Miller et al. 1997 ; Miller and de Pablo 2000 ; Salvetti et al. 2007 ). All crystalline samples exam-ined presented endothermic enthalpies of dissolution, ranging from about 17 to 90 J/g. Conversely, the same samples in the amorphous state showed an exothermic response, with enthalpies between − 30 and − 80 J/g. This difference is often attributed to the higher entropy and internal free energy of the metastable amorphous material, leading to enhanced dissolution rate and chemical reactivity relative to the ther-modynamically more favorable and stable crystalline state (Hancock and Zografi 1997 ; Hancock and Parks 2000 ; Hancock 2002 ; Wong et al. 2006 ). In the case of lactose, it was proposed that the interactions within the crystalline material were stronger than the hydration process; thus, its dissolution resulted in an endothermic response (Harjunen et al. 2004 ). In the case of an amorphous material, the solid interactions might be weaker compared with the crystalline counterpart, resulting in a more spontaneous dissolution characterized by a release of energy during the process. In all cases, crystallization leads to a change of the physical structure, possibly leading to impaired rehydration properties (Roos and Karel 1991 ).

The heats of solution (or dissolution) of many pharmaceutical sub-stances also have been measured directly by solution calorimetry, generally with a different experimental setup. Quantitative analysis of polymorphs, solvates, and amorphous forms in pharmaceutical sub-stances has been performed using solution calorimetry (Giron et al. 2004 ). Studies of polymorphism by solution calorimetry also have been reviewed (Giron 1995 ).

The effect of moisture content on the thermodynamic response of dissolving powders was studied, and a signifi cant change in the enthalpy of dissolution (less exothermic) for amorphous lactose samples stored at increasing humidities was reported (Hogan and Buckton 2000 ). For α - cyclodextrin, the enthalpy of solution reverted from exothermic for a dry sample to endothermic for a hydrated sample (Bastos et al. 2004 ).


This effect was explained in terms of the exothermic nature of the wetting process, resulting in a less exothermic response for a sample that has already adsorbed moisture from the environment, compared with a completely dry sample. It is often argued that the sorption of water by a dry powder is the fi rst stage of wetting and that the fi rst few molecules adsorbed on the surface are responsible for the greatest part of the overall exothermic wetting response (Buckton 1995 ; Hancock and Shamblin 1998 ; Hancock and Dalton 1999 ; Hogan and Buckton 2000 ). This effect might be observed and can be quantifi ed when a solid sample is exposed to water vapor. However, if the solid is undergoing dissolution, the distribution of moisture in the bulk of the material will govern the calorimetric response as consecutive layers of the solid are exposed to the liquid medium (Marabi et al. 2007b ). Assuming that the moisture is uniformly distributed in the solid matrix, a less exothermic response is expected as the moisture content increases, and a transition from an exothermic to an endothermic process might be observed at a limiting moisture content (Bastos et al. 2004 ). In a dry glassy material, the measured enthalpy might result from the bonds created between the water molecules and the hydrogen - bonding sites (Miller and de Pablo 2000 ; Lechuga - Ballesteros et al. 2002 ). Haque and Roos (2006) recently speculated that freeze - dried materials might have a higher amount of hydrogen bonding sites available for sorption of water molecules than spray - dried materials. Indeed, even a small increase in moisture content of a drug above a critical value of 3% was shown to signifi cantly decrease its dissolution rate (Li et al. 2004 ). Consequently, a less exo-thermic response due to either higher moisture content of the powder or a reduced number of available hydrogen bonding sites could have major implications in the wetting mechanism of food powders, which in turn might slow down their dissolution process (Marabi et al. 2007b ). Faster dissolution kinetics were also reported to be correlated with more exo-thermic processes under different conditions (Hancock and Parks 2000 ; Terada et al. 2000 ; Marabi et al. 2007a ).

Isothermal solution calorimetry was also used to derive the wettabil-ity of fi nely divided solids (Lazghab et al. 2005 ) and the surface energy of solids such as silica, quartz, kaolinite, and illites (Zoungrana et al. 1994 ; Medout - Marere et al. 1998 ), fl uorinated carbons (Spagnolo et al. 1996 ), talc and quartz (Malandrini et al. 1997a,b ), and fumed silica (Yan et al. 2000 ). By using nondissolving liquids, it is possible to measure the enthalpy of immersion that can then be used to derive


the contact angle between the particulate solid and the liquid (Spagnolo et al. 1996 ; Adamson and Gast 1997 ). The application of immersion calorimetry could therefore circumvent the diffi culties associated with assessing contact angles of food powders. Several reviews on the use of isothermal calorimetry in different (e.g., pharmaceutical, microbio-logical) applications are available (Buckton 1995 ; Wads ö 1997 ), indi-cating the wide applicability and useful information that can be obtained from this technique, which surprisingly, has not been exploited in the fi eld of food science.

The effects of fat content on the dissolution enthalpy and kinetics of a model food powder were reported previously (Marabi et al. 2008 ). Typical dissolution calorimetry curves for fi ve freeze - dried samples with increasing fat contents are shown in Figure 9.8 .

Dissolution of all the powders resulted in an exothermic response. Increasing the amount of fat in the samples is clearly related to a decrease in the amount of heat released during the dissolution process. The decrease in the enthalpy of dissolution ranged from 61.9 to 32.4 J/g for samples with 0.7% and 45.0% of fat, respectively (Figure 9.9 ). This, in turn, is related to the dissolution kinetics of the powders, which also was shown to be signifi cantly affected by increasing amounts of fat in the samples (Marabi et al. 2008 ). However, when the enthalpy of dissolution is normalized by the amount of fat material, it can be

Fat content

EXO

0.7%14.3%

0 500

No

rmalized

Heat

Flo

w [

W/g

]

0.16

0.14

0.12

0.10

0.08

0.06

0.04

0.02

0.00

–0.021000 1500

Time [s]

2000 2500

29.3%35.7%45.0%

Figure 9.8. Typical dissolution calorimetry curves of a model food powder with increasing amounts of fat. From Marabi et al. (2008) , with permission .


seen that very similar values are obtained for all the samples tested (Figure 9.9 ). This fact indicates that the fat has a minimal contribution to the overall enthalpy measured, which is in agreement with the slight endothermic response measured when pure fat is mixed with water (Marabi et al. 2008 ).

The effects of different moisture contents and the physical state of maltodextrin (MD) and skim milk powder (SMP) also were studied (Marabi et al. 2007b ). Namely, three different conditions were studied: after samples were freeze - dried (FD), after equilibration at 54.4% rela-tive humidity, and after FD (water activity again less than 0.01) of the equilibrated sample. The calorimetric curves are shown in Figure 9.10 a and b. The dissolution was found to be exothermic for all the tested samples, and the curves showed similar shapes. For both samples, a clear decrease in the response was observed when the FD and the equilibrated samples are compared. When the latter were FD again, the MD resulted in a curve almost identical to that obtained with the original FD samples. In contrast, the SMP sample showed only an intermediate response between those of the FD and the equilibrated samples. The enthalpy of dissolution decreased (became less exother-mic) about 12 - and 20 - fold from the FD state compared with the equilibrated state for the MD and SMP samples, respectively.

00.7 14.3

Enthalpy of dissolution normalized by fat content

Total Enthalpy of dissolution

ΔH

dis

s (

J/g

)29.3

FAT (%)

35.7 45.0

–10

–20

–30

–40

–50

–60

–70

–80

–90

Figure 9.9. Enthalpy of dissolution of a model food powder with increasing amounts of fat expressed as J/g of sample and as J/g of nonfat material.


After freeze - drying the equilibrated MD sample, more than 98% of the original enthalpy was recovered. These data indicate that the enthalpy of dissolution is a strong function of the water content and that it is reversible to some extent upon drying of the powder, provided a phase change does not occur in the solid. The part of enthalpy not recovered could be explained by the residual amount of water in the

MD DE21 - FD

MD DE21 - aW 0.54

Exo

↑

Exo

↑

2500 300020001500

Time [s]

1000

a

b

0.16

0.14

0.12

0.10

0.08

0.06

0.04

0.02

0.00

0.14

0.12

0.10

0.08

0.06

0.04

0.02

0.00

500

No

rmali

zed

Heat

Flo

w [

W/g

]N

orm

alized

Heat

Flo

w [

W/g

]

0

2500 300020001500

Time [s]

10005000

MD DE21 - aW 0.54 & FD

SMP - FDSMP - aW 0.54

SMP - aW 0.54 & FD

Figure 9.10. Typical dissolution calorimetry curves of the maltodextrin DE21 (a) and skim milk powder (b) at different conditions. All the samples showed an exothermic response; however, increased moisture content or recrystallized lactose led to a less exothermic dissolution process. From Marabi et al. (2007b) , used with permission .


sample. In contrast to the MD powder, the equilibrated SMP sample that was subsequently FD recovered only approximately 50% of the value observed for the originally FD sample. This effect is related to the nonreversible process of lactose crystallization that occurred in the SMP sample. The enthalpy of dissolution was signifi cantly reduced due to the presence of the crystalline material for which an endother-mic effect is expected upon dissolution, with reported values between approximately 52 and 60 J/g (Miller and de Pablo 2000 ; Harjunen et al. 2004 ). An approximate reduction of about 8 J/g in the calorimetric response for each 1% of adsorbed water was calculated when both the MD and SMP FD samples were compared with the equilibrated ones. These values are comparable with those observed for amorphous lactose (Hogan and Buckton 2000 ), sucrose and trehalose (Miller and de Pablo 2000 ) having different moisture contents, for which an approximate reduction of 3 – 5 J/g in the net enthalpy for each 1% of water that is adsorbed was reported.

Clearly, the physical state of the samples affected the enthalpy of dissolution, with amorphous samples showing a more exothermic response than partially crystalline samples. It was observed optically that the dissolution kinetics were hastened by more exothermic responses, which contributed to an overall spontaneous process, whereas less exothermic responses clearly resulted in a much slower dissolution rate.

In conclusion, the current approach demonstrated that the thermo-dynamic aspect has a crucial importance in the dissolution of food powders and that isothermal calorimetry should be implemented if optimization of the dissolution process is required.

Specifi c Heat

Calorimeters are often used to determine specifi c heats of foods because process engineers request such information when installing new equip-ment. The methods and a synthesis of results have been presented in the literature (Mohsenin 1980 ). The values obtained vary between 1.25 J g − 1 K − 1 for very dry food products (without fat) and 4.18 J g − 1 K − 1 for water. The moisture content of a food has thus a strong infl uence on its specifi c heat value. The specifi c heat values of a solid increase with temperature; for water, the value is approximately constant between 0 ° C and 100 ° C.


For high - precision measurements, synthetic sapphire is used as the standard (Raemy and Lambelet 1982 ).

Sometimes specifi c heat measurements are combined with glass temperature measurements for food - water systems (Pyda 2002 ) as well as for polymers (Marti et al. 2006 ).

Heat of Combustion

During burning of a food product, a large amount of energy is liber-ated. Values determined with calorimetric bombs are about 39 kJ g − 1 for fat, 23 kJ g − 1 for protein, and 17 kJ g − 1 for carbohydrate.

Related Techniques

Some related thermal analysis techniques, such as dynamical mechani-cal analysis (DMA) or dynamical mechanical thermal analysis (DMTA) give rheological rather than thermal information. Also, thermogravim-etry, sometimes coupled with gas analysis instruments, can provide a better interpretation of calorimetric curves (e.g., when studying hydrated carbohydrates) by indicating weight losses associated with the observed thermal phenomena.

Microscopy techniques, sometimes performed at different tempera-tures with a hot stage microscope, are often also very helpful in obtain-ing a clear interpretation of calorimetric curves.

Concerning lipids, as assignments of DSC signals may be ambigu-ous due to the high number of thermal events, calorimetry often needs to be combined with XRD (Chung and Caffrey 1992 ; Keller et al. 1996 ) or even synchrotron XRD when transformations are rapid. Recent experiments combining DSC and synchrotron XRD have revo-lutionized the study of lipid crystallization (Ollivon et al. 2001 ; Kalnin et al. 2002 ; Lopez et al. 2002 ). DSC can also be used simultaneously with microscopy to identify morphologies associated with polymorphs (Rousset et al. 1998 ).

Fat crystallization in oil - in - water emulsions has been followed by DSC in combination with either nuclear magnetic resonance spectros-copy ( Ö zilgen et al. 1993 ) or XRD (Awad et al. 2001 ; Ollivon et al. 2001 ).


Interest of Calorimetry for the Food Industry

The main reason for studying exothermic phenomena (carbohydrate decomposition, fermentation, oxidation) of food with calorimetric techniques is to improve process safety. A precise knowledge of the temperature ranges of these phenomena and the corresponding enthal-pies allows adequate safety measures to be taken and helps to diminish the number of fi res, explosions, and bursting of autoclaves in food - processing plants (see Chapter 15 ). It leads thus to a favorable context for avoiding personnel injuries and for loss prevention. Exothermic reactions are desired in some processes, for example, roasting, and have to be avoided in others, such as high - temperature drying.

Endothermic phenomena, such as melting, must be avoided or moni-tored to obtain the correct product. For example chocolate should melt in the mouth but not on the hand; thus, the cocoa butter in chocolate should mainly be in the crystalline form V (melting temperature range between 27 ° C and 37 ° C).

Glass transition temperatures are, in combination with water activity and moisture content, of great interest for studying adequate storage conditions of milk powders, for example by avoiding browning and caking. But this concept also has allowed the development of coffee and milk products with desired amount of foam at the top when recon-stituted in water or milk. These products, which are typical examples of the so - called glass transition technology, are presently very popular with consumers.

Specifi c heats of foods are requested by process engineers for the design and installation of new thermal equipment (Kaletun ç 2007 ).

Heat of solution of food powders in water or milk is of major impor-tance for the food industry because dissolution speed, which is directly related, is one of the main criteria of the consumer when selecting a food powder for preparing an instant drink.

Conclusion

In addition to this information, which can be obtained with the help of calorimetric techniques, some databases such as the European database EVITHERM can be used to fi nd thermal parameters about food and nonfood products ( www.evitherm.org ). The Internet sites of the com-mercial instrument suppliers also give much valuable information.


Even so, new products, new conditions, and new processes push performance of more calorimetric measurements and testing of new instrumentations, such as isothermal titration calorimetry (ITC), which is of interest for studying such interactions as that between proteins and active substances, or dielectric spectroscopy, which is of interest in the study of proteins or the amorphous state of carbohydrates.

A new trend is to use coupled systems for performing evolved gas analysis in relation to the thermal signal; for example, thermogravim-etry coupled with mass spectrometry is commonly used today.

Thermal analysis and the study of foods have been mutually benefi -cial: thermal analysis and calorimetry in general have brought much information to food science and processing technology, whereas the study of foods, as the samples are easily available, has allowed the development and the promotion of many calorimetric techniques.

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Chapter 10

Shelf Life Prediction of Complex Food Systems by Quantitative Interpretation

of Isothermal Calorimetric Data

Simon Gaisford , Michael A.A. O ’ Neill , and Anthony E. Beezer

237

Introduction 237 Qualitative Studies 239 Quantitative Studies 245 Empirical Model Fitting 246 Modeling Based on Reaction Kinetics 249 Reactions That Proceed to Completion 252

Calculation of the Initial Calorimetric Signal θ 0 252 Calculation for the Reaction Order 252 Calculation for the Total Heat Released for Complete Reaction 253 Calculation for Reaction Half - Life 254 Calculation for Rate Constant 254 Calculation for Reaction Enthalpy 255

Reactions That Proceed to a Point of Equilibrium 255 Test for Complete Reaction 255 Determination of K 255 Calculation of Q T 256

Summary 261 References 261

Introduction

The analysis of foods and food components presents a considerable challenge, not least because they may contain many ingredients, be


diffi cult to handle, and may derive from one or more natural sources (which inherently introduces batch - to - batch variability in composi-tion). Indeed, in many respects foods may be considered as complex biomaterials. While qualitative analyses may suffi ce for some quality assurance protocols (e.g., texture, feel, density), it is often desirable to obtain quantitative data; this is particularly true where food authentic-ity (i.e., whether a food substance conforms to its label claim) is required. In this context, it is often diffi cult or complex to use classical assay techniques. It is not necessarily straightforward, for example, to quantify banned additives in foodstuffs using high - performance liquid chromatography or spectroscopy because of the need to isolate the analyte prior to analysis. A discussion of the merits of various analyti-cal tools for determination of food authenticity can be found in Reid et al. (2006) .

We have long argued that calorimetry, in particular isothermal calo-rimetry, is ideally suited to the study of complex samples because it offers many unique advantages. First, the measured parameter is heat. This is advantageous because heat can be considered as a universal indicator of change (and note here that change in this context can mean both physical and chemical processes). Thus, it will unquestionably be the case that a sample can in principle be studied with calorimetry. Whether a meaningful interpretation can be made depends only upon the magnitude of the heat change and the number of events occurring (discussed further below). Second, the instrument requires no sample treatment or preparation. The entire sample is housed within an ampoule and monitored in situ; or, if the sample is too large for the ampoule, a fraction is enclosed that is representative of the whole. Thus, the need to isolate a particular analyte, as in a chromatographic assay, is obvi-ated. Finally, the technique does not require optical clarity of a sample and is invariant to physical form, which means that any complex mate-rial can be studied in its entirety.

There are two principal calorimetric techniques; isothermal calorim-etry (IC) and differential scanning calorimetry (DSC). With the former, the sample is monitored at a constant temperature; and with the latter, the sample is subjected to a controlled temperature ramp (usually increasing). Unsurprisingly many of the calorimetric studies of food-stuffs have used DSC (see Raemy et al. 2004 ; Schiraldi 2004 ) or temperature - modulated DSC (De Meuter et al. 1999 ). This is, perhaps, not unexpected since such instruments are uniquely well suited to

Shelf Life Prediction of Complex Food Systems 239

evaluating the consequences of cooking on foodstuffs. There have been much fewer such detailed studies devoted to IC investigations — the subject of this chapter — and yet the type of information obtainable from isothermal investigation can be vastly more enlightening because it is, of course, just such studies of storage over time that may defi ne food stability and shelf life. It may, too, allow investigation of those reactions that occur isothermally at high temperature, such as the cooking temperature. In addition to these patent benefi ts, recent work has resulted in a new range of analysis methodologies with which to recover quantitative reaction parameters from IC data; these data can be used to inform sample design and improvement. A review of the application of isothermal calorimetry to food stability is thus the topic of this chapter.

Qualitative Studies

Quantitative data interpretation usually requires some prior knowledge of the properties of the system under investigation (e.g., number of reacting systems, reaction pathway) as well as a model with some factual basis. While for simple (one - or two - component) systems quan-titative interpretation may be possible, inevitably with more complex systems there will be instances in which qualitative outcomes can be indicative of change despite the absence of detailed interpretation of the experimental data. Much of the literature in this fi eld thus reports qualitative data, and it is here that this discussion starts.

Qualitative analyses are usually applied to complex systems because in such cases it is not technically possible to interpret multifaceted power - time data. Consequently, this discussion starts with applications of calorimetry to bioprocesses and bioprocessing in which microorgan-isms are used in the production of, or as an ingredient in, foodstuffs. It is not a simple matter to measure effi cacy of bioprocesses in situ because of the heterogeneity of the system (which may change viscos-ity, density, and optical transparency). Conventional microbiological assay techniques, such as plating and counting, are time consuming, do not provide information on real - time growth in the actual process environment, do not compensate for the fraction of the microbial load that is dead or not viable, and exclude any contribution to effi cacy caused by physical effects (such as thickening of the medium).


With calorimetry, the power signal is quantitatively proportional to the number of viable organisms in the sample, which means the tech-nique is immediately appropriate for counting cell numbers. Indeed, this is one of its primary advantages over plate - counting methods, because the calorimeter only reports heat changes from living organ-isms and is not subject to error from inclusion of nonviable cells. The technique addresses all of the concerns raised earlier because it is not dependent on the physical form of the sample and does not impose a requirement for optical clarity.

An important consideration when using calorimetry to monitor growth of organisms is the repeatability of the growth curves. There can be enormous variation between bacteria cultured on different days; if this variability results in greater heat changes than the process under investigation, no conclusions can be drawn. In attempting to overcome this limitation, Beezer et al. (1976) and Cosgrove (1979) developed procedures to allow storage of frozen inocula of various organisms, including Saccharomyces cerevisiae and Pseudomonas aeruginosa . In this method, a batch of organisms is grown overnight in a bacterial culture medium. Late exponential growth phase cells are harvested, washed in phosphate - buffered saline (PBS), resuspended in 15% v/v glycerol to an organism density of 10 8 cfu/ml, and frozen in aliquots (1 ml) over liquid nitrogen. Organisms can be stored for more than 6 years in this frozen state and remain viable after thawing with less than 1% decrease in viability.

The benefi t of using frozen inocula is very tight reproducibility of the growth curves. An example (in this case for P. aeruginosa ) is shown in Figure 10.1 . Taking the total area under the growth curve (total heat output) as an indicator of organism numbers shows repro-ducibility to 6.3% (3.51 ± 0.22 J). For S. cerevisiae , the reproducibility is normally no greater than ± 1.5% from the mean and never greater than ± 2.5% from the mean (Perry et al. 1979 ). The growth curve rep-resented in Figure 10.1 is complex and characteristic of organism growth in an undefi ned medium with restricted oxygen (the ampoule is sealed and the oxygen level is limited to that dissolved in the medium and present in the headspace). In brief, the initial exponential phase represents aerobic metabolism, which is then followed by a switch to anaerobic metabolism. Subsequent peaks and troughs represent sequen-tial use of the major carbohydrate sources typically found in a complex growth medium.


A good example of the very valuable data that can be produced from qualitative analysis of isothermal calorimetric data is provided by studies of the growth of yeasts on a variety of substrates (Perry et al. 1981, 1983 ). Yeasts play an important role in many food industries, including baking and brewing, and are used in other industries as well, for example, as a means of producing ethanol from renewable sources or for use in single - cell protein (SCP) production. The authors took three commercial strains of S. cerevisiae (D1, distilling; D2, pressed baking; and D3, active dried baking) and two NCYC strains (87, dis-tilling and 239, brewing) and analyzed their growth curves with fl ow microcalorimetry (an isothermal calorimeter equipped with a cell that allows medium to be fl owed through from an external reservoir). In a glucose medium the growth curves of the three baking strains showed little differentiation, although the growth curve of the brewing strain was notably different. In a maltose medium, good differentiation was observed between all strains. One immediate outcome from these data is the ability to use a simple medium (maltose) to characterize the properties of a new strain of S. cerevisiae and select it as appropriate for either baking or brewing.

When complex media are considered the utility of calorimetric study increases further. Perry et al. (1983) showed that growth of yeast in

Figure 10.1. Power - time data showing the growth curves for six repeats of P. aeruginosa .


glucose - maltose media did not show sequential use of the carbohy-drates; rather, both sugars were exhausted after the initial growth phase. Discrimination of strains was achieved by adding maltotriose to the medium.

Molasses, a waste product of the sugar industry, is a raw material used in both baking and brewing because it is highly suitable for yeast growth. The suitability of a particular batch of molasses is dependent upon the technical processes by which the sugar was manufactured and also by agronomic factors of the cane and beet production. As a result, the growth of yeast in a particular molasses batch can be highly vari-able, and there will be a direct impact on the production costs and quality of the baked or brewed product. A method that allows rapid assessment of the quality and suitability of a batch of molasses is thus highly desirable and diffi cult to achieve by classical physical and chemical means. It is known that only part of the carbohydrate reser-voir in molasses is of nutritive value to the yeast. As a consequence, analytical data are not suffi cient to characterize molasses batches from a bioavailability point of view. Perry et al. (1981) show how interpreta-tion of calorimetric growth curves of S. cerevisiae in molasses samples could be used rapidly to identify optimal growth media, growth curves in molasses identifi ed as “ poor ” being distinct from growth in molasses typed as “ adequate - to - good. ”

A similar approach was used more recently by Alklint et al. (2005) to predict the shelf life of carrot juice. Here, growth of the mesophilic and psychrotrophic fl ora in the carrot juice was monitored after manu-facture at 17 ° C (the highest permitted temperature for elevated stabil-ity tests of chilled foodstuffs in Sweden) with an isothermal calorimeter. It was found that the heat outputs recorded correlated with plate counts, indicating that the calorimetric approach was valid. The initial cause of spoilage was found to be the same at 17 ° C as at 8 ° C (the maximum permitted storage temperature of chilled foodstuffs in Sweden), and the data were found to be suitable for predicting shelf lives.

An area of growing interest is foods that offer some health benefi ts, so - called functional foods. Prime among these are products that aim to modulate the microfl ora of the gastrointestinal (GI) tract. Bacterial numbers vary along the human GI tract, increasing from about 10 3 g − 1 of gastric content to about 10 6 – 10 7 g − 1 of content at the terminal ileum (Gorbach et al. 1967 ). The colon, in particular, is a complex and diverse microbial ecosystem, bacterial numbers reaching 10 11 – 10 12 g − 1


of gut content (Cummings and Macfarlane 1991 ). This makes the colon the most metabolically active organ in the body. In addition, the number of bacterial species is large (several hundred), creating a diverse microfl ora. The numerically predominant bacteria ( ∼ 30%) are the bacteroides, although other genera, such as bifi dobacteria, eubacteria, lactobacilli, streptococci, and clostridia, are also present (Fooks et al. 1999 ).

The microfl ora has many roles and is important to general health and well - being. A primary function is in fermenting undigested food-stuffs that have not been absorbed in the upper GI tract. Typically, substrates are carbohydrates (including starches, dietary fi ber, and oli-gosaccharides). The principal fermentation products are short - chain fatty acids (SCFA); these are subsequently absorbed by the body and metabolized, which contributes to the energy gain of the host (Cummings 1995 ) and means the relationship between host and micro-fl ora is symbiotic. Another important function is to inhibit the growth of pathogenic organisms. However, in the absence of suffi cient carbo-hydrate, certain bacteria, such as clostridia , switch to protein fermenta-tion, which produces harmful nitrogenous metabolites (including biogenic amines, indoles, and ammonia).

To minimize this effect, the body excretes a number of mucins, which are high in carbohydrates and encourage the growth of certain microbial species. The UK food market is currently replete with prod-ucts designed to modulate the gut microfl ora; usually, such products are supplemented with either prebiotics or probiotics. Probiotics are live bacteria of species deemed to be benefi cial to health when ingested; usually, lactic acid bacteria (LAB) are indicated and they are com-monly found in yogurts, where they convert lactose to lactic acid, giving the product its distinctive sour taste and acting as a preservative. Prebiotics are nondigestible food ingredients that stimulate the growth of one or more benefi cial bacteria in the colon and were fi rst defi ned by Gibson and Roberfroid (1995) . Several potential nondigestible foodstuffs have been investigated for prebiotic effi cacy, but to date the only substances for which credible data showing a favorable effect are available are the oligosaccharides (Delzenne and Roberfroid 1994 ).

However, it is not a simple matter to show a benefi cial prebiotic effect in vivo, primarily because of the sheer complexity of the gut microfl ora and its effect on physiological response. Typically, an increase in the number of bifi dobacteria excreted in feces has


historically been accepted as proof of effi cacy, but it remains to be proven either that an increased level of bifi dobacteria in the gut cor-relates with an increase in health or well - being or that the number of Bifi dobacteria is a good biomarker for gut health (Ouwehand et al. 2005 ).

In addition to the complexity of determining an in vivo effect, many probiotic foodstuffs contain multiple cultures, and it is diffi cult simply to demonstrate that these do not compete with each other. While clas-sical microbiological techniques do not allow direct observation of such processes, calorimetry potentially does. For example, Sch ä ffer et al. (2004) used isothermal calorimetry to monitor the growth of two cultures commonly found in probiotic dairy products. In addition to the added probiotic, Prebiolact (a probiotic organism developed by the Hungarian Dairy Research Institute), the product contains Hansen ’ s CHN - 22 mesophilic butter culture to add aroma. It was found that the growth curves of the two organisms were distinct, the growth curves showing maxima at 4.5 h and 6.5 h for the Prebiolact and the butter culture, respectively. When studied in combination, the growth curve became more complex, but the maxima at 4.5 h and 6.5 h were still present, indicating the two organisms did not interfere with each other.

Qin et al. (2006) demonstrated the effect of the water solubility of various chitosans on antimicrobial activity using isothermal calo-rimetry. The growth curves of Staphyloccus aureus, Escherichia coli, and Candida albicans were monitored in the presence and absence of various chitosans (different molecular weights and N - acetylated derivatives). It was found that water - soluble chitosans had no signifi -cant antimicrobial activity and in some cases increased the growth of C. albicans . Water - insoluble chitosans were found to have anti-microbial activity, however, when in an acidic medium. Chitosans with molecular weight of approximately 5 × 10 4 were found to be most active.

Riva et al. (2001) used isothermal calorimetry to evaluate the shelf life of whole eggs, fresh milk, and carrots. The three foodstuffs were all subject to microbial spoilage, and microbial growth was monitored. Complex growth curves were recorded, which showed peaks associ-ated with development of the microbial population. With an increase in temperature, the peaks appeared earlier and were sharper, indicating faster microbial growth. The authors selected the point at which the time derivative of the power signal was at a maximum as the stability


time. This qualitative outcome was supplemented with data from plate counting and pH measurements, which confi rmed the increasing power signal derived from an increase in the microbial population.

Galindo et al. (2005) investigated the change in the metabolic response of foodstuffs pre - and postprocessing by isothermal calorim-etry. Here, minimal processing operations were considered, such as peeling, grating or shredding. The effect of these operations can impact the quality of the product through several means; changes in respiration rate, increased biochemical reactions through wounding stress and microbiological storage. The rate of many of these processes is depen-dent upon the surface area of the product, and the authors found rela-tionships between surface area and thermal power for several vegetables (carrots, rutabagas, and potatoes). It was also possible to monitor enzy-matic browning of potatoes and the effect of browning inhibitors, such as citric acid or ascorbic acid.

Quantitative Studies

Quantitative (which may mean simply determining the number and nature of reaction processes through to recovery of descriptive reaction parameters, such as rate constants, enthalpies, and activation energies) interpretation of complexity in isothermal calorimetric data is demand-ing. Primarily, this is because, as noted, heat is a universal accompani-ment to chemical and physical change. Its ubiquitous nature means that the measured signal is a composite of the powers arising from each of the individual events occurring (which can involve physical, as well as chemical, change). Any meaningful analysis thus has the primary objective of determining the number of processes contributing to the overall data. This number alone is a useful basis for quantitative inter-pretation, because it could be indicative of a reaction pathway and hence could give some indication of mechanism. Once the number of steps is known, the data must be deconvoluted into their component parts. Once the individual processes are identifi ed, analysis to recover quantitative reaction parameters is usually much more straightforward. Over the past 15 years, a number of methods have been proposed for quantitative analysis of calorimetric data, from simple model fi tting to model - free chemometric analysis. Here, these approaches are reviewed and illustrated with examples of application to food products.


Empirical Model Fitting

In the complete absence of any knowledge of the processes occurring in the sample, or in the case where an equation based on a known mechanism is not available, the simplest approach to modeling calori-metric data is to fi t the data to an empirical equation (i.e., an equation that conveniently fi ts the data but does not attempt to describe the reaction processes occurring). A simple example would be to use an exponential decay model, such as that shown in Equation 10.1 :

y y A ext= −

−

0 . (10.1)

where x and y are the plotted variables, y 0 is the initial value of y , and A and t are constants. For example, some calorimetric data are repre-sented in Figure 10.2 . The exact process that gave rise to these data is not important, but it shall be assumed that they represent the heat output of a partially completed reaction. The data can be fi tted to Equation 10.1 by least - squares minimization to determine the equation parameters that describe them. Once these values have been deter-mined, it is a simple matter to extend the data to the time at which the power signal falls to zero (shown by the dotted line in Figure 10.2 ). The area under the dotted line thus represents the total heat, Q , which

Figure 10.2. The fi t of calorimetric data to an exponential model and the subsequent extrapolation to power = 0.


would be generated by the reaction if it went to completion. From this information, it is easy to determine the percentage of reaction com-pleted at any time, t , by taking fractional areas. Hence, it would be possible to determine quantitative shelf - life data, even though the reac-tion processes remain unclear.

Similarly, taking the total heat output of a process (the area under the power - time data) allows quantifi cation of the amount of material reacted if the reaction enthalpy is known. This approach can be used for quite sensitive analysis of components in foodstuffs when an enzyme is used to degrade a specifi c ingredient, because the enzyme is highly specifi c for a particular substrate and the calorimeter is able to quantify the reaction in a complex sample without the need for isola-tion or purifi cation of the reacting components.

A number of groups have reported enzymatic methods for quantifi -cation of ingredients in foodstuffs. For instance, Forte et al. (1996) demonstrated the utility of using lipolytic enzymes for quantifi cation of fat content in food. Here, pancreatic lipase was used to catalyze the hydrolysis of triglycerides (lipases) in a number of food products (oils, milk, and milk derivatives). A calibration curve was prepared with tributyrin as a model substrate prior to work on the foodstuffs, and it was shown that the calorimetric response of the enzymatic turnover reaction was linear up to a substrate concentration of 15 mM. When analyzing oils, the authors had to dilute the samples 100 - fold in buffer prior to analysis to ensure the experiment was conducted in this linear region. Two classes of oils were studied: olive oil (extra virgin, olive, and husk) and seed oil (peanut, soya, and mixed seed). It was found that it was possible to differentiate oils from different classes, but not oils within one class, as their heat outputs were the same within experi-mental error. It was found possible to differentiate milk samples (whole, semi - skimmed, and skimmed) and also to quantitate fat content to 0.1 g/l. In milk derivatives (yogurts), again a linear response was found for fat contents from zero to 3 g/l, although the slope of the line was observed to be lower than that of the milk samples. The authors ascribed this to the fact that yogurts have a microbial population also capable of producing lipase enzymes that would compete with the assay reaction.

More recently, the same group demonstrated a similar approach for the quantifi cation of L - malic acid (Antonelli et al. 2008 ). In this case, fumarase is used to catalyze the dehydration of L - malic acid. The


calibration curve was found to be linear to 2.68 g/l; above this concen-tration, nonlinearity was found as a result of the inverse reaction also catalyzed by the fumarase. The authors compared the assay with a classical spectrophotometric approach and noted the calorimetric tech-nique gave equivalent answers but with no requirement for sample isolation or cleanup. L - malic acid was successfully quantifi ed in a range of beverages (red and white wines, soft drinks, and apple juice) and solid food products (apples, mandarins, and powder for making carbonated water).

The use of ascorbate oxidase to quantify ascorbic acid (vitamin C) concentrations has attracted much attention. Antonelli et al. (2002) found a calibration curve of calorimetric response versus ascorbic acid to be linear between ascorbic acid concentrations of 3 – 270 mg/l. Similarly, O ’ Neill (2004) used this approach to investigate the quality of fresh orange juices and determined the kinetics of ascorbic acid degradation (discussed further below).

A derivative technique that can offer some useful insight into the properties of food substances is solution calorimetry. In this experi-ment, a solid (usually) sample is introduced to a solvent, and the heat change upon dissolution is recorded (Royall and Gaisford 2005 ). The technique is useful because small changes in the physical form of a material, such as a change in polymorph or percentage of amorphous content, will result in a change of dissolution enthalpy. Marabi et al. (2007) have shown that solution calorimetry can be used to study the dissolution of two food powders, maltodextrin and skimmed milk. As the moisture content of the powder increased a concomitant decrease in the exothermic dissolution, heat was seen. The effect was reversible if the moisture content was reduced unless crystallization occurred in the sample. The authors used real - time video analysis to follow dis-solution kinetics and related these to the calorimetric data.

However, it is possible to interpret the calorimetric data quantita-tively to recover dissolution parameters. Conti et al. (2006) , in a study of the dissolution of various hydroxymethylcellulose (HPMC) poly-mers, demonstrated how it is possible to convert the calorimetric dis-solution data into a plot of swelling ratio; this was achieved by assuming that the total heat output during the experiment, Q (obtained by integra-tion of the power - time data), corresponded to complete swelling, while the heat output to any time t ( q t ) corresponded to the fraction of swell-ing that had occurred to that point. Hence, a plot of q t / Q versus time


gave a set of data that represented the swelling response; typical plots of the dissolution profi le of the polymer and the swelling ratios are shown in Figures 10.3 and 10.4 . The swelling curves were then ana-lyzed using a power - law model. It was shown that for all polymers dissolution occurred immediately following hydration of the polymer, although there was a rate dependence upon both polymer grade and solution pH.

Modeling Based on Reaction Kinetics

The next step in complexity from the use of empirical models is to fi t data to models based on reaction kinetics. Clearly, for this approach to be valid, the mechanism of reaction must be known prior to analysis. This approach is also not appropriate for those processes that involve physical change (although if the chemical reaction data can be isolated from any physical change data this approach is legitimate). While the use of such models has been documented in full elsewhere (Gaisford and O ’ Neill 2006 ), it is appropriate to discuss the simplest case, a single step, A → P reaction process here; logical extension of the analysis can be made to more complex reaction schemes. The rate of disappearance of reactant A , or the buildup of product P , is given by

Figure 10.3. Power - time data for the dissolution of various grades of HPMC into water. Reprinted from Conti et al. (2006) , with permission from Elsevier.


− =d

d

A

tkAn

(10.2)

Since

A A x= −0 (10.3)

Then

dd 0xt

k A x n= ⋅ −( )

(10.4)

Where d x /d t is the rate of reaction, k is the rate constant, A 0 is the initial quantity of reactant A that is available for reaction, x is the quantity of reactant A reacted at time t, and n is the order of reac-tion. It should be noted that from a mathematical perspective, n may have any value, integral or nonintegral, but to have meaning as a kinetic model only integral values are considered. Immediately, there-fore, it is possible to check the validity of a model against real data, for the values of any reaction orders obtained should be integral. For any given reaction that has gone to completion, the total heat evolved

Figure 10.4. The swelling profi les of various grades of HPMC calculated from the power - time data shown in Figure 10.1 . Reprinted from Conti et al. (2006) , with per-mission from Elsevier.


during the course of the reaction, Q , must be equal to the product of the enthalpy of reaction, Δ H , and the number of moles of material reacted, A 0 :

Q A H= ⋅0 Δ (10.5)

It follows that

q x H= ⋅Δ (10.6)

where q is the heat evolved at time t . Substituting q / Δ H for x and Q / Δ H for A 0 in Equation 10.4 and rearranging gives

ddqt

k H Q qn n= = ⋅ ⋅ −( )−Φ Δ 1

(10.7)

where θ is the calorimetric power (in watts). Assuming n ≠ 1, integra-tion of Equation 10.7 gives

Q q k t H n Qn n n−( ) = ⋅ ⋅ ⋅ −( ) +[ ]− − −Δ 1 11

11 (10.8)

This expression may be substituted into Equation 10.7 to give

Φ Δ Δ= ⋅ ⋅ ⋅ ⋅ ⋅ − +[ ]− − − −k H k t H n Qn n nnn1 1 1 11( ) (10.9)

Equation 10.9 describes calorimetric data that derive from reactions that follow a single - step, solution phase process. Calorimetric data from such a reaction may be entered into a suitable mathematical package and, by least - squares minimization, the reaction parameters may be quantifi ed. This process was fi rst described by Bakri (1988) and was later extended by Willson et al. (1995) .

A further consideration of these equations results in methods to calculate directly the parameters of interest. Knowing these values reduces the burden on the fi tting program and increases confi dence in the values returned. Here, methods to calculate parameters for single - step reactions that proceed to completion or to equilibrium are dis-cussed. A discussion of the extension of these principles to more complex reaction schemes can be found in Gaisford and O ’ Neill (2006) .


Reactions That Proceed to Completion

Calculation of the Initial Calorimetric Signal θ 0

In most calorimetric experiments, data over the fi rst few minutes are lost (because the sample is prepared externally to the instrument and there will be a heat of friction introduced by loading), which means it is not possible to measure the value of the power signal at t = 0 ( θ 0 ). The value must therefore be inferred in some way from the recorded data set. A convenient strategy is to apply a polynomial series (usually fourth order is suffi cient) to the fi rst 10 h of any experimental data set and to extrapolate to a value of θ 0 .

Calculation for the Reaction Order

Knowledge of the value of n is vital because it can give some mechanistic insight to a data set that contains no molecular informa-tion. By selecting two power values from the calorimetric data, θ 1 and θ 2 , it has been shown that the ratio of the two associated times, t 1 and t 2 , for θ 1 and θ 2 is dependent only on the order of reaction (Willson 1995 ). Rearrangement of Equation 10.9 for the two time points gives

t k H

Q

k H n

n

nn n

n1

11

1

1

1 1= ⋅

⎛⎝⎜

⎞⎠⎟ −

⋅ ⋅ −( )−

−

−

−

ΦΔΔ

(10.10)

and

t k H

Q

k H n

n

nn n

n2

21

1

1

1 1= ⋅

⎛⎝⎜

⎞⎠⎟ −

⋅ ⋅ −( )−

−

−

−

ΦΔΔ

(10.11)

and therefore,

tt

nn

nn

2

1

2

1

1

1= ( )

( )

−

−

Φ

Φ

(10.12)


It follows that the order of reaction may be determined from knowl-edge of the value of t 2 / t 1 . This is most easily achieved through the use of a suitable mathematical worksheet. The values of θ 1 and θ 2 are converted into percentages of the initial calorimetric signal ( θ 0 ) and, by using the worksheet, a table of values of t 2 / t 1 calculated from Equation 10.12 , can be constructed as a function of the rate constant for a particular pair of ( θ 1 / θ 0 ) and ( θ 2 / θ 0 ) ratios. The experimental t 2 / t 1 constant may then be compared with the table of t 2 / t 1 values, and the order of reaction can be determined.

Calculation for the Total Heat Released for Complete Reaction

In the unlikely case that the reaction progresses to completion within the experimental measurement period, Q is simply the area under the power - time curve. More commonly, this is not the case, and it becomes necessary to calculate a value for Q from the experimental data.

Recall Equation 10.7 :

Φ Δ= −( )−k H Q qn n1 (10.7)

If two values of θ at different points along the calorimetric curve are taken, and their associated values of q are noted such that

Φ Δ11

1= − −( )−H k Q qn n (10.13)

Φ21

2= − −( )−H k Q qn n (10.14)

Then,

ΦΦ

1

2

1

2

= −( )−( )

Q qQ q

n

n

(10.15)

ΦΦ

1

2

1

1

2

⎛⎝⎜

⎞⎠⎟ = −( )

−( )n Q q

Q q

(10.16)

Setting


ΦΦ

1

2

1⎛⎝⎜

⎞⎠⎟ =

n

R

(10.17)

The value for Q can then be derived from Equation 10.18 .

Q q Rq

R= −( )

−1 2

1 (10.18)

At this point, it should be noted that the values of q 1 and q 2 must include the area under the curve for the missing data always encoun-tered at the start of any calorimetric experiment. It is clear that this value cannot be measured directly, but it can be approximated (with reasonable accuracy) from the calculated value of θ 0 and the accom-panying polynomial equation used to derive it, described earlier.

Calculation for Reaction Half - Life

The reaction half - life, t1 2, is defi ned as the time taken for half the reactable material to be consumed. Assuming n ≠ 1, it is easily calcu-lated from

t

n kA

n

n1 2

1

01

2 11

=−( )

−( )[ ]−

−

(10.19)

Hence, if k (see below), A 0 , and n are known, then t1 2 can be

calculated.

Calculation for Rate Constant

Because the reaction order, n , is known, a kinetic equation that describes the reaction can easily be written. This equation can then be manipu-lated to reveal the rate constant. Taking a second - order reaction as an example,

Φ Δ= −

+⎛⎝⎜

⎞⎠⎟k H A

kA t0

0

2

1 (10.20)


If the ratio of two data points θ 1 and θ 2 at times t 1 and t 2 are taken, Equation 10.21 is obtained:

ΦΦ

1

2

0 2

0 1

11

= +( )+( )kA tkA t

(10.21)

This equation can be expanded and rearranged to yield a quadratic expression in terms of k :

k RA t A t k RA t A t R202

12

02

22

0 1 0 22 2 1 0−( ) + −( ) + −( ) = (10.22)

This quadratic function can then be solved in the normal way.

Calculation for Reaction Enthalpy

Earlier it was shown that the total heat output, Q , is given by Equation 10.5 . The reaction enthalpy is then easily calculated.

Reactions That Proceed to a Point of Equilibrium

It is important to know whether the reaction under study reaches completion or equilibrium, because knowledge of the number of moles of material reacted is essential to calculate the correct value for the reaction enthalpy. Note: In the following treatment, Q represents the amount of material that will react and is distinct from Q T which is the value of Q if all the sample (i.e., A 0 ) content had reacted.

Test for Complete Reaction

A test for complete reaction is to study the reaction (for identical loads) over a range of temperatures. Noting that because the equilibrium constant (if one exists), K , will change as a function of temperature, Q must also vary as a function of temperature. If the reaction proceeds to completion, then the value of Q will remain constant across the temperature range and will be equal to Q T for all temperatures.

Determination of K

For the reaction A ⇔ B , the equilibrium constant is given by

K A

B= [ ]

[ ] (10.23)


At equilibrium [ B ] is given by Equation 10.24 and [ A ] is described by Equation 10.25 .

B Q

H[ ] =

(10.24)

[ ]A A BT= − [ ] (10.25)

Hence,

A Q

HQH

T[ ] = −

(10.26)

Thus,

K

QH

QH

QH

T

=−⎛

⎝⎞⎠

⎛⎝

⎞⎠

(10.27)

And therefore,

K Q

Q QT=

−( ) (10.28)

Equation 10.28 can be written for studies at different temperatures, T m ( m = 1, 2, 3, etc.). Note that Q T will remain constant for all values of T providing that the reaction mechanism does not change and that there is no dependence of change in heat capacity, Δ C p , over the chosen temperature range.

Equation 10.28 then permits K to be calculated for any chosen tem-perature. However, a value for Q T is required in order to effect this calculation. The value of Q T is not directly available from the calori-metric data and must be calculated separately.

Calculation of Q T

The calculation of Q T is undertaken by consideration of the effect of change in temperature on the equilibrium constant. The van ’ t Hoff equation states


δ

δ

lnK

T

HR

( )⎛⎝

⎞⎠

= −1Δ

(10.29)

If it is assumed that Δ H is independent of temperature, then integration of Equation 10.29 gives

KK

HR T T

1

2 1 2

1 1= − −⎛⎝⎜

⎞⎠⎟

(10.30)

The ratio KK

1

2

will be equal to KK

2

3

, for temperatures T 2 and T 3 , if the

temperatures T 1 , T 2, and T 3 are such that Equation 10.31 is true.

T TT T

T TT T

1 2

2 1

2 3

3 2−( )=

−( ) (10.31)

(For instance, if T 1 = 298 K and T 2 = 303 K, then T 3 will be equal to 308.5 K). If this condition is met, then Equation 10.32 can be written.

KK

QQ QQ

Q Q

KK

QQ QQ

T

T

T1

2

1

1

2

2

2

3

2

2

3= −( )

⎛⎝⎜

⎞⎠⎟

−( )⎛⎝⎜

⎞⎠⎟

= = −( )⎛⎝⎜

⎞⎠⎟

QQ QT −( )⎛⎝⎜

⎞⎠⎟3

(10.32)

This can be solved for Q T , (for temperatures m = 1, 2, and 3):

Q Q Q Q Q Q Q Q

Q Q QT = + −−

⎛⎝⎜

⎞⎠⎟

22

1 22

3 3 2 1

22

3 1

2

(10.33)

The enthalpy is now accessible since Q T and A T are known:

QA

HT

T= Δ

(10.34)

Once Δ H is known, A can be calculated:


QH

Am

Δ=

(10.35)

Once these parameters are known, it is a simple matter to calculate the rate constant, k , using the methods described earlier.

Since Q T and Q are accessible the equilibrium constant, K , can be derived from Equation 28 and hence the values for the Gibbs function and entropy are readily obtained from Equations 10.36 and 10.37 respectively.

ΔG RT K= − ln (10.36)

Δ Δ ΔH GT

S−( ) =

(10.37)

The calculated values of K will allow an “ internal ” check on the valid-ity of the procedure through the derived value of Δ H . A plot of

ln K versus 1T

will yield a straight line with slope − ΔHR

. If the procedure

is invalid, the mechanism changes with T , or there is a signifi cant temperature dependency of heat capacity (over the temperature range used), the van ’ t Hoff plot will not be linear, and/or the value of Δ H will not match that derived from Equation 10.34 .

Since Q and Q T are known and if Δ H is accurately recovered, then it is possible quantitatively to determine the reactable material content in a heterogeneous sample.

Although there are fewer examples in the literature, kinetic inter-pretation of calorimetric data for foodstuffs has been reported. Tortoe et al. (2007) report a kinetic analysis of the osmotic dehydration of apples, bananas, and potatoes. In osmotic drying, the foodstuff is placed in a concentrated sugar solution; water is then drawn out of the foodstuff as a result of osmosis. A side effect of osmotic drying is the ingress of sugar into the foodstuff, resulting in a dried, sweetened material. For all apple samples, a three - phase signal was observed. The initial, large, signal was assumed to be rapid transfer of water and sugar from the cut surfaces of the food. The second phase represented move-ment of water from intracellular spaces, and the fi nal phase represented movement of water from extracellular spaces. For banana and potato samples, a two - phase signal was observed. In all cases, the processes


were fi rst order, which meant that a simple plot of ln (power) versus time allowed recovery of the rate constants from the gradients of the respective phases. The rate constants changed in magnitude in the order k 1 > k 2 > k 3 and increased with temperature. Arrhenius plots of the data suggested a linear relationship for Golden Delicious apples and potatoes and nonlinear behavior for Cox apples and bananas.

As noted, O ’ Neill (2004) studied the kinetics of ascorbic acid in freshly squeezed orange juice. This is more complex than simple studies of the oxidation of ascorbic acid because spoilage of fresh orange juice generally results both from oxidation of ascorbic acid and from degradation of pectin by pectin methyl esterase. Although it is still the subject of debate, it is thought that the degradation of pectin results in protein agglomerates that settle, resulting in clarifi ca-tion of the juice. Clarifi cation is a major cause of spoilage in orange juice, and strenuous efforts are made to eliminate it. This can be done in a number of ways. Classically, the juice is pasteurized to denature the protein as well as destroy the microbial fl ora. However, heat treatment adversely affects the fl avor and aroma of the juice and is generally unsatisfactory if the juice is to be marketed as fresh - squeezed. Alternatively, the juice is frozen during storage and transportation, reducing the activity of the enzyme and other degradative processes. This is costly and usually only viable for concentrated juice. An ideal solution would be some natural additive that does not adversely affect the desired quality parameters for orange juice but does inhibit pectin methyl esterase, in quantities that are not harmful, reducing cloud destabilization. Selection of such a material starts with the ability quantitatively to monitor the reaction processes directly within orange juice samples.

Both the oxidation reaction and the enzyme reaction are pH depen-dent, with the oxidation reaction favoring alkaline conditions and the enzyme reaction favoring more neutral conditions. By buffering the orange juice at pH 7.0, O ’ Neill (2004) held the enzyme reaction at almost optimal conditions, while the oxidation reaction was more favored compared with the naturally more acidic pH of orange juice. The calorimetric data revealed two distinct fi rst - order phases (Figure 10.5 ). The fi rst reaction progressed with a rate constant of 3.9 ( ± 1.0) × 10 − 5 s − 1 , and the second progressed with a rate constant of 2.7 ( ± 0.3) × 10 − 5 s − 1 .


Analysis of the pectin/pectin methyl esterase reaction revealed it to progress with a rate constant of 3.9 ( ± 0.3) 10 − 5 s − 1 , identifying this as the fi rst process in the orange juice sample. A similar analysis of the oxidation of ascorbic acid in buffer gave a rate constant of 3.0 ( ± 0.3) 10 − 5 s − 1 , highlighting this as the second process. A further benefi t of the kinetic analysis was the recovery of the enthalpy of oxidation of ascorbic acid, − 155 ± 25 kJ/mol. This information allows the quantifi -cation of ascorbic acid content in the orange juice sample. From Figure 10.5 , it can be seen that the calorimetric output from approximately 100,000 s onwards can be solely attributed to the oxidation of ascorbic acid. This being so, then the calorimetric output at t = 0 ( θ 0 ) for the oxidation reaction can be obtained from the y intercept of the ln θ versus t plot. This value is quantitatively proportional to the amount of ascorbic acid of the sample:

A

kH= Φ0

(10.38)

where A is the quantity of ascorbic acid.

Figure 10.5. Observed calorimetric output for buffered orange juice. Reprinted from O ’ Neill (2004) , with permission.


Summary

The study of foods and food ingredients is complicated, largely because physical form impacts the selection and use of any analytical assay technique. Because it measures a universal property and is invariant to physical form, calorimetry offers an exciting opportunity for the inves-tigation of foodstuffs. The full capabilities of calorimetry have not yet been exploited in this area, although the number of reported applica-tions is increasing. Here, it has been shown that calorimetry can be used to study a huge variety of samples, from simple ingredients (such as ascorbic acid) to complex biological processes. Data interpretation ranges from qualitative to quantitative, the analysis methods being dependent upon the complexity of the sample. However, a combination of clever experimental design, sample preparation, and data analysis mean that quantitative outcomes are increasingly available from calo-rimetric data matrices, and the application of the technique to foods and food ingredients can only continue to increase.

References

Alklint , C. , Wads ö , L. , and Sj ö holm , I. 2005 . Accelerated storage and isothermal microcalorimetry as methods of predicting carrot juice shelf - life . J Sci Food Agri , 85 : 281 – 285 .

Antonelli , M.L. , Spadaro , C. , and Tornelli , R.F. 2008 . A microcalorimetric sensor for food and cosmetic analyses: L - malic acid determination . Talanta , 74 : 1450 – 1454 .

Antonelli , M.L. D ’ Ascenzo , G. Lagan à , A. , and Pusceddu , P. 2002 . Food analyses: A new calorimetric method for ascorbic avid (vitamin C) determination . Talanta , 58 : 961 – 967 .

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Cosgrove , R.F. 1979 . Long - term storage of microorganisms used in antimicrobial effectiveness tests . J Assoc Off Anal Chem , 62 : 1188 – 1190 .


Cummings , J.H. 1995 . Short chain fatty acids . In: Human Colonic Bacteria: Role in Nutrition, Physiology and Pathology , G.R. Gibson and G.T. Macfarlane , editors. CRC Press : Boca Raton, FL .

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Forte , L. , Vinci , G. , and Antonelli , M.L. 1996 . Isothermal microcalorimetry as a useful tool for fat determination in food . Anal Let , 29 : 2347 – 2362 .

Gaisford , S. and O ’ Neill , M.A.A. 2006 . Pharmaceutical isothermal calorimetry . Informa Healthcare , New York .

Gibson , G.R. and Roberfroid , M.B. 1995 . Dietary modulation of the human colonic microbiota: Introducing the concept of prebiotics . J Nut , 125 : 1401 – 1412 .

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O ’ Neill , M.A.A. 2004 . PhD dissertation . University of Greenwich : London . Ouwehand , A.C. , Derrien , M. , de Vos , W. , Tilhonen , K. , and Rautonen , N. 2005 .

Prebiotics and other microbial substrates for gut functionality . Cur Opi Biotechnol , 16 : 212 – 217 .

Perry , B.F. , Beezer , A.E. , and Miles , R.J. 1979 . Flow microcalorimetric studies of yeast growth: Fundamental aspects . J Appl Bacteriol , 47 : 527 – 537 .

Perry , B.F. , Beezer , A.E. , and Miles , R.J. 1981 . Microcalorimetry as a tool for evalu-ation of complex media: Molasses . Microbios , 32 : 163 – 172 .

Perry , B.F. , Beezer , A.E. , and Miles , R.J. 1983 . Characterization of commercial yeast strains by fl ow microcalorimetry . J Appl Bacteriol , 54 : 183 – 189 .

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thermodynamic and kinetic parameters from isothermal heat conduction microcalorimetry: Applications to long - term reaction studies . J Phys Chem 99 : 7108 – 7113 .

Chapter 11

Use of Thermal Analysis to Design and Monitor Cereal Processing

Alberto Schiraldi , Dimitrios Fessas , and Marco Signorelli

265

Introduction 265 Starch 268 Proteins 272 Nonstarch Carbohydrates 276 Process Applications 278 Conclusions 285 References 285

Introduction

The term thermal analysis (TA) means the record of any physical prop-erty during a given thermal treatment under strict temperature control.

The main physical property that is monitored in this way is enthalpy (and the related property, heat capacity); the relevant thermal analysis is named calorimetry and is performed in a few well - defi ned condi-tions, each with a specifi c name: isothermal calorimetry (IC), differ-ential scanning calorimetry (DSC), temperature - modulated DSC (TMDSC), and modulated adiabatic scanning calorimetry (MASC).

Another physical property that is traditionally included in the TA realm is mass; the relevant analysis is named thermogravimetry (TGA) and is currently used to monitor the mass loss upon heating the sample. Its trace is often transformed into that of its time derivative (DTG).

The mechanical properties of a given sample, like Young ’ s modulus, E , elastic and storage moduli, G ′ and G ″ , respectively, are those


recorded with the dynamic mechanical analysis (DMA, or DTMA) and thermomechanical analysis (TMA). Applications related to the dielec-tric character of the sample go under the name of dielectric analysis (DEA).

Every type of TA has been used in the study of food stuffs and food processes. Calorimetry was used to determine heat capacities (Schwartzberg 1976 ; Noel and Ring 1992 ), describe the behavior of frozen ice - forming food systems (Goff, Montoya, and Sahagian 2002 ), and study many phase transitions (Roos 1995 ), including those that imply a heat capacity drop with no transition enthalpy, like the so - called glass transition (Slade and Levine 1991 ). Since most of these changes imply a substantial modifi cation of the mechanical and rheo-logical properties of the sample, DMA and DEA also were used to describe these phenomena (Laaksonen and Roos 2000 ; Vodovotoz, Hallberg, and Chinachoti 1996 ). A rather recent improvement of ther-mogravimetry is the Knudsen TGA that allows determination of the water activity along the whole dehydration pattern of a given sample in isothermal conditions (Schiraldi and Fessas 2003 ).

Because of the intrinsic heterogeneity of many food materials, a suitable sampling is crucial for the reliability and the reproducibility of the recorded TA traces, which usually is much poorer than for puri-fi ed chemicals. Too - small samples may not represent the food inves-tigated; smashed or powdered products may miss some important feature of the starting material, for example, the surface area/volume ratio that affects the rate of diffusion - limited processes and the effects related to the overall texture of sample that can be missed when the material is ground or fi nely sliced (Riva, Schiraldi, and Piazza 1994 ).

The simultaneous occurrence of different changes within a given sample is another reason for the humping and bumping trend of the TA traces from food samples. In DSC, baseline shifts due to glass transitions are often partially overlapped to peak signals of fi rst - order transitions and chemical, biochemical, and microbe - sustained reac-tions. This explains why DSC investigations were often devised to allow just qualitative tests or comparison between samples and why tentative evaluations of the enthalpy changes, as in the case of starch gelatinization and retrogradation, are often a major cause of discrep-ancy between different authors who made different choices for the baseline trends. As a consequence, DSC has been considered a sub-stantially qualitative analytical tool by many food scientists and technologists.

Use of Thermal Analysis 267

Fortunately, this is not true, provided that the operators are well trained and adequately educated in physical chemistry. Suitably per-formed TA indeed provides a real insight into the processes that occur within a food system during a thermal treatment, as the relevant change of the involved physical property is directly related to the laws of thermodynamics and kinetics. For example, any thermal effect, Δ H , when coupled with a temperature value, T , is a measure of the stability of the system, thanks to the relationship,

∂∂

⎛⎝

⎞⎠

⎡⎣⎢

⎤⎦⎥

= −T

GT

HTp

Δ2

(11.1)

where G is the Gibbs function. Moreover, the rate of heat release or adsorption, Q = d Q /d t (where Q and t stand for heat and time, respec-tively), is directly related to the rate of the underlying process,

�Q H

t= ⎛

⎝⎞⎠Δ d

d,α

(11.2)

similar expressions holding for the change rate of practically every physical property recorded during a temperature scan.

These laws are of great help in recognizing the “ true ” trend of a baseline (Roduit 2000 ) and the “ true ” transition points, because they provide means to make reliable predictions. The problem that cannot be easily overcome concerns the number of partially overlapped phe-nomena that take place during a given heating or cooling run, each relevant to a single component of the system, as in the case of cereal - based food products. A deconvolution of the relevant trace therefore becomes a necessary step of the data treatment (Schiraldi 2003 ).

This chapter reports a short critical review of the applications of TA to the study of cereal - based food and related processing. The main concern of these products deals with the transitions of starch in the presence of other components of the system, such as proteins and nonstarch carbohydrates, which compete for the available moisture and/or affect its partition between the different regions or phases of a given food system, and lipids, which are responsible for specifi c interactions that produce peculiar DSC signals. Since water is ubiqui-tous in most food systems, it is expedient to look at the changes that take place within a given sample by focusing on water and directly detecting changes in the state of water that are concomitant with


changes relevant to the other components of the system. Particular emphasis therefore is given in this chapter to approaches based on the choice of water as a “ probe compound. ”

Starch

Starch is a supramolecular substance that nature assembles within the seeds of cereals and pseudocereals, legumes, and tubers. Starch may not, therefore, be referred to as a chemical compound: it has neither a defi -nite molecular mass nor a fusion point, and it does not react with other substances until its granular structure is rotten and its glucose poly-mers, amylose and amylopectin, are exposed to the surrounding envi-ronment. Starch chemistry starts with surface processes that take place at the pores and defects of the granule structure, which remains practi-cally unaffected at temperatures below 45 ° C, even with excess water.

An aqueous suspension of starch granules is easy to prepare and investigate with TA techniques. A superfi cial wetting takes place when starch granules are dispersed in excess water. The process is exother-mic and can be detected with isothermal calorimetry (IC) and suitable mixing cells (Riva, Piazza, and Schiraldi 1991 ).

DSC (and related variations) equipment (Liu and Shi 2006 ) is instead enough to monitor changes that take place when the starch suspension is heated (Figure 11.1 ).

Across a 30 ° C range, starting from an onset temperature that depends on the vegetal origin of the starch investigated (e.g., 45 ° , 50 ° , and 65 ° C, for potato, wheat and rice, respectively), water enters the granule and disaggregates the internal crystal regions that are mainly formed by the side branches of amylopectin molecules. The whole starch granule is transformed in a swollen jelly ghost of the original hard and birefringent body. A gentle stirring turns the starting suspension into a dispersion of amylopectin gel and amorphous insoluble amylose. The two glucose polymers are mutually incompatible (Kalichevsky and Ring 1987 ), which means that they may not stay in the same phase, being competitors for the available moisture.

Because of this, the system is rather heterogeneous and unstable. On further heating, the amylopectin gel turns into a sol, while around 90 ° C, nucleation of amylose crystals can take place. The whole process, currently dubbed “ starch gelatinization, ” is therefore a multistep, fully


irreversible (Figure 11.1 ) transformation of the starting suspension of starch granules. The corresponding DSC trace presents a wide endo-thermic signal, with maximum at a temperature that depends on the moisture content of the system: the lower the moisture, the higher the temperature of the maximum (Figure 11.2 ).

Moreover, mainly because of the different sizes of the starch gran-ules, samples with low moisture content (in any case, never below 70%) show a high T shoulder, which has been attributed to the delayed degradation of larger granules (Biliaderis, Page, Maurice, and Juliano 1986 ), although the reliability of this interpretation has yet to be con-vincingly demonstrated. A further endothermic signal (in the 90 ° – 115 ° C range) can often be observed (Figures 11.1 and 11.2 ) in the DSC of aqueous suspensions of starches extracted from some fl ours that contain lipids: the signal corresponds to the fusion of amylose - lipid complexes formed in the course of the starch gelatinization (Eliasson 1994 ; Bulpin, Welsh, and Morris 1982 ; Le Bail 1999 ).

Because amylopectin molecules can carry phosphate groups (the number of which once again depends on the vegetal origin of the starch), the properties of the gel obtained are affected by the ionic strength and the pH of the surrounding medium. In particular, when

Figure 11.1. DSC trace of a suspension of starch (Merck product) granules in excess (72.5%, w/w) water, recorded on heating (upper curve) and cooling (lower curve) at 5 ° C min − 1 scanning rate (authors ’ unpublished data).


the system is cooled to room temperature (and below), the amylopectin gel becomes the matrix of growing crystals that entrap water and cause the system to harden. Although the structure of the original granules is by no means restored, the term starch retrogradation is currently used to indicate this process. It can be easily monitored with DSC investigations (Riva, Fessas, and Schiraldi 2000 ). It must be under-stood that the crystal phases formed are suffi ciently extended to allow X - ray detection (Zobel 1988 ; Yuryev et al. 2004 ), but they should be envisioned as islets of ordered arrays of chains with disordered moi-eties dangling out of their boundaries, rather than as well - shaped microcrystals. Crystal islets of different extension are present through-out a given amylopectin gel, and various types of crystal structures may be formed, named according to the respective X - ray diffraction patterns A, B, C, etc. (Zobel 1988 ; Yuryev et al. 2004 ). For these reasons, the progress of the crystal growth can imply the coexistence of different polymorphs that have different thermal stability, and when the “ retrograded ” system is warmed, the “ fusion ” process therefore encompasses a temperature range between 30 ° and 90 ° C (Figure 11.3 ) (Riva, Fessas, and Schiraldi 2000 ).

If the system undergoes a suitable annealing treatment, formation of amylose crystals becomes possible (Wasserman et al. 2007 ). Their fusion takes place in the 125 ° – 140 ° C range and can be easily detected with a DSC investigation (Wasserman et al. 2007 ). Figure 11.4 shows

Figure 11.2. DSC traces of suspensions of starch (Merck product) granules at various water contents (72.5%, 61.1%, and 42.5%, w/w) recorded on heating at 5 ° C min − 1 scanning rate (authors ’ unpublished data).


two DSC traces obtained from the same aqueous starch suspension: the dashed profi le refers to the fi rst heating (2 ° C min − 1 ) run, during which starch gelatinization and fusion of amylose - lipid complexes occurred, whereas the full line profi le refers to the heating run per-formed after a suitable annealing treatment (Wasserman et al. 2007 ) that favors the formation of amylose crystals. The latter shows the endothermic effect related to the fusion of amylopectin crystals, while a second endothermic peak with onset at a much higher temperature is related to the fusion of amylose crystals. The growth of these crystal phases prevents the formation of amylose - lipid complexes (no signal appears in the expected temperature range).

These amylose crystals are resistant to the amylase assisted hydro-lysis: the hosting system has therefore received the misleading name of “ resistant starch, ” which meets industrial and commercial needs rather than the chemical truth.

A starch gel heated in an open crucible releases its moisture with a diffusion - limited mechanism (Fessas and Schiraldi 2005 ). This means that solvation water can be easily exchanged with water engaged in the structure.

Figure 11.3. Endothermic effect related to the fusion of amylopectin crystals formed within the starch gel phases of a bread dough (data are given as excess heat capacity, namely heat fl ux, divided by the scanning rate × sample mass product). Dashed curve describes the trend of the of the relevant enthalpy versus the storage time. The records were obtained at 2 ° C min − 1 heating rate. Modifi ed from Riva et al. (Riva, Fessas, and Schiraldi 2000 ).


In short, all these starch transformations take place in a cereal fl our dough in the presence of many other substances that compete for the available moisture or may directly interact with starch carbohydrates. The relevant DSC traces should therefore be interpreted by taking into account the possible interactions between different dough components.

Blends of fl ours from cereals and pseudocereals allow dough prepa-rations in which many interactions are expected (Fessas et al. 2008 ). These “ complications ” can be addressed by considering the potential role of the other nonstarch main components of a dough, namely, proteins, nonstarch carbohydrates, and lipids.

Proteins

In most cereals, both globular and networking proteins are present. The former, dubbed albumins and globulins after Osborne (Osborne 1924 ), can be either enzymes or carriers, are soluble in aqueous media, and

Figure 11.4. DSC traces obtained from an aqueous starch suspension (heating rate 2 ° C min − 1 ; data are transformed in apparent heat capacity, dividing the heat fl ux by sample mass × heating rate). The dashed line refers to the fi rst heating. The heavy line corresponds to the record of the reheating run performed after a suitable annealing treatment (Wasserman et al. 2007 ). Notice that the fusion of the amylopectin crystals (formed during the annealing) occurs at lower temperature, while no signal relevant to amylase - lipid complexes appears and that a new endotherm occurs at much higher T corresponding to the fusion of amylose crystals (formed during the annealing).


are therefore easily extractable. The latter tend to form wide three - dimensional meshes that entrap aqueous phases and separated bodies, like starch granules. Gluten is the most important representative of this family: it is not soluble in water and therefore can be separated by washing a dough loaf with hot water to wash away starch carbohy-drates and globular proteins. Minor amounts of nonsoluble compounds remain entrapped in the gluten meshes and represent the unavoidable “ contaminants ” of any gluten preparation. Details about the chemistry of the cereal proteins are beyond the scopes of this chapter; suffi ce it to say that when a dough is prepared from a cereal fl our, globular proteins play mainly a surfactant role that is crucial in stabilizing the air bubbles formed in a leavening loaf, whereas gluten is responsible for the overall rheological behavior of the system.

Both globular proteins and gluten fi x water molecules, although in rather different ways. The former are normally solvated at the surface polar groups and modify their own solvation shell when unfolding and denaturation take place. Gluten instead uses water molecules as bridges between the next neighboring chains (Belton 1999 ) and develops an extended network, due to a large number of hydrogen bonds (Figure 11.5 ).

Because of this, gluten can entrap large amounts of interstitial water within its meshes. Some disulphide bonds provide more robust inter - and intrachain links and affect the overall extensibility of the network. Any elongation strain squeezes the interstitial water out of the meshes so that the next neighboring chains become closer to one another; when the strain is allowed to relax, water can reoccupy the interstitial regions and make the meshes swell back to the starting size. This view has been recently perfected (Belton 2005 ; Kontogiorgos and Goff 2006 ), and it is still adequate to suggest general guidelines for our understand-ing of the competition of various fl our components for the available water that undergoes displacement between the coexisting aqueous phases of a cereal fl our dough from its early mixing, to proofi ng and baking, or freezing and storing.

This a fundamental issue to be considered. Because of the thermo-dynamic incompatibility (Tolstoguzov 2003 ) between proteins and carbohydrates, as well as between different carbohydrates (Liu and Shi 2006 ), a fl our dough is indeed a dispersed system in which several aqueous phases coexist that can exchange the solvent between one another.


This partition of water can be detected with thermogravimetry inves-tigations (Fessas and Schiraldi 2005 ): the related DTG trace shows a broad signal that can be deconvoluted in two or more components, each relevant to a given water fraction (Figure 11.6 ).

The water fraction that sustains the evaporation at mild temperatures mainly belongs to the imbibing moisture (in the earliest steps) and by the separated (because of the thermodynamic incompatibility of their solutes, such as carbohydrates and globular proteins) aqueous phases: the solvent that evaporates from one aqueous phase is quickly replaced by the water migrating from any neighboring aqueous phase. As a result, the dehydration of the samples looks like a single process gov-erned by the core - to - surface diffusion of moisture (Fessas and Schiraldi 2005 ). The rest of the water (about 15% of the starting overall dough moisture) tends to remain close to the network forming polymers (mainly gluten) and can be stripped away only at temperatures above 100 ° C.

Starch gelatinization is mainly sustained by the former water frac-tion when the dough is being baked. Because the starch gelatinization

Figure 11.5. Naive picture of the water bridges and direct hydrogen bonds between hydro - compatible biopolymer chains (which can be referred to as carbohydrates or gluten proteins).


Figure 11.6. Deconvolution of the DTG trace obtained from a wheat dough sample. The high - temperature peak can be related to the water fraction tightly trapped within the gluten meshes, while the lowest broad peak is related to the imbibing water that is released through a Fickian diffusion mechanism. The other minor peaks should be referred to as contributions from moderately bound water fractions.

rate is strongly dependent on the starch/water mass ratio, the loss of water reduces the degree of gelatinization attained (Fessas and Schiraldi 2000 ) (Figure 11.7 ).

The effect is obviously different in the various regions of a dough loaf, being more severe at the surface where dehydration is faster and the crust is being formed.

Two main situations can be envisaged: namely, before and after the onset of the starch gelatinization. Wetted starch granules fi x a rela-tively small amount of water, while most of the solvent is engaged by salts, globular proteins, water - soluble nonstarch carbohydrates, and gluten. The hydration shell water is the poorly available fraction of the overall dough moisture, whereas the rest of the solvent, including water molecules trapped in the gluten meshes and in the nonstarch carbohy-drate - entangled coils, can be driven toward the starch granules once the gelatinization onset is trespassed. This onset in turn depends on the available moisture: addition of small molecular mass solutes that can fi x water produces a delay of the starch gelatinization, while addition of hydratable polymers, such as water - soluble arabinoxylans, has no signifi cant effect. The reason for this difference is due to the different effect on the relative humidity (RH) of the system: for a given dry matter content, an aqueous solution of simple sugars or salts shows a


much lower RH than an aqueous solution of arabinoxylans (Fessas and Schiraldi 1998 ).

Combinations of TA with other techniques, such as NMR relaxom-etry (Vittadini et al. 2003 ; Lopes-Da-Silva et al. 2007 ), MRI (Hills 1998 ), and NIR (Huang et al. 2003 ), provide the experimental evidence of these changes, even in a very complex system such as staling bread (Morgan, Fourneaux, and Stanley 1992 ; Schiraldi, Piazza, and Riva 1996 ). They can, however, be better understood once the principle of thermodynamic incompatibility is put at work.

Nonstarch Carbohydrates

Cereal and pseudo - cereal fl ours contain some nonstarch carbohydrates coming from various regions of the seed. A fraction of them is not water extractable and therefore is segregated from the aqueous phases of the dough: the role of this fraction has little relevance to the physical behavior of the other dough components (safe for some sensorial prop-erties of the fi nal products).

Figure 11.7. Deconvolution of the DTG trace of a manually mixed dough with 42% moisture content (30 mg sample, 2 ° C/min heating rate) and starch gelatinization of a dough undergoing heating at the same heating rate in a open DSC pan. Modifi ed from Fessas and Sachiraldi (Fessas and Schiraldi 2000 ).


The water - extractable fraction conversely plays a crucial role on the overall viscosity of the dough and to the partition and displacement of water (Fessas and Schiraldi 2001a ; Courtin and Delcour 1998 ; Fessas and Schiraldi 2001b ).

Although only one water molecule per single sugar monomer can be engaged to solvate these polymers, much larger amounts of mois-ture are trapped within their entanglements and can be easily displaced under the effect of chemical potential gradients.

Because a fl our dough is a quite viscous environment, only short - range displacements are indeed allowed. This explains why when the dough is being dehydrated, as in the course of a TGA run, the water displacements through the samples are rapidly hindered by the concur-rent increase of the viscosity.

The fl our of some gluten - free cereals and pseudo - cereals, such as buckwheat, soy, and amaranth, can trap water because of different proteins but cannot form a stable dough because the polymer chains do not arrange themselves in a web. This water fraction is therefore much more mobile than the moisture trapped within gluten meshes. The relevant DTG trace therefore shows a single “ diffusional ” (see above) peak (Figure 11.8 ). When one of these fl ours is mixed with wheat fl our to form a blend, then the dough can be formed upon knead-ing. The relevant DTG trace (Figure 11.8 ) shows two peaks: the high T signal is once again attributed to the moisture fi xed by the gluten

Figure 11.8. DTG traces of dough prepared with buckwheat (a), wheat (b), and 50% (w/w) buckwheat/wheat fl our (c) (authors ’ unpublished data).


meshes, although the temperature gap between the two maxima, which is related to the looseness of the gluten network, is smaller than in the trace of a wheat fl our dough. This effect is mainly related to nonstarch and nongluten proteins, which form separate phases (droplets) that do not allow gluten to attain an extended and tight reticulation (Fessas and Schiraldi 1998 ).

Process Applications

The information collectable with TA investigations can be directly used to improve food formulations and process conditions. Of great interest are the use of the so - called state diagrams that highlight the role of the glass transition temperature as a border between high - and low - molecular - mobility regions, which have been collected in Roos ’ s book (Roos 1995 ). Few other examples are helpful to the reader.

Any given thermal treatment, such as cooking, baking, frying, etc., corresponds to a thermal history experienced by the system. A direct reproduction of such history can be diffi cult to plan when using TA equipment. Nonetheless, one can inscribe any thermal history in a TTT (time temperature transformation) diagram that can be defi ned on the experimental basis of TA evidence of a given transformation respon-sible for a specifi c signal in the TA record. Starch gelatinization moni-tored with DSC is a good example. The relevant signal is an endothermic shouldered peak, the area of which, once divided by heating - rate × starch - mass product, corresponds to the specifi c enthalpy change, Δ H (joules per gram of starch units), accompanying the gelatinization. The peak area swept at any given T within the ( T onset , T end ) range is related to the progress degree, α , achieved at that T :

α T( ) = partial peak area

total peak area.

(11.3)

To account for the effect of the moisture content, the reference to be chosen is the thermal effect recorded in excess water, Δ ∞ , which is larger than those observed at low water contents since only a fraction of the overall starch undergoes gelatinization (or experience a com-plete transformation) when moisture is less than 40% (w/w). The total peak area must accordingly be scaled with respect to the area of the


peak recorded in excess moisture. To clarify with an example, if the recorded Δ H is 0.75 Δ H ∞ , then

α T( ) = ×partial peak area

total peak area0 75.

(11.4)

Since starch gelatinization is an irreversible transformation, the rele-vant DSC signal refl ects the process kinetics and is affected by the heating rate, β = d T /d t . The higher the heating rate, the higher the end temperature of the signal (there is also a shift of the apparent onset temperature that is of minor concern for the present discussion, however). One therefore has to defi ne the temperature range where starch gelatinization takes place for each β and the progress degree within that range:

�Q H

tH

TdTdt

HT

= ⎛⎝

⎞⎠ = ⎛

⎝⎞⎠

⎛⎝

⎞⎠ = ⎛

⎝⎞⎠Δ Δ Δd

ddd

dd

α α β α

(11.5)

where Q is the recorded heat fl ux per unit mass of starch and Δ H is the related enthalpy change.

Once the DSC runs at the selected heating rates are performed, the increase of α on sweeping the related peaks can be determined: more precisely, one has to detect the temperatures at which α attains some given levels (say α = 0.1, 0.2, 0.3, etc.) for each considered β (Figure 11.9 ).

The TTT diagram is a T - versus - t plot, where the straight lines cor-responding to the various heating rates considered in the experimental design of the DSC investigations have to be drawn fi rst.

The selected iso - α temperatures (see above) have to be marked along the corresponding straight line in the TTT diagram. Eventually, a map of iso - α points is obtained that can be used to draw iso - α curves (Figure 11.10 ).

Remember that the maximum attainable α does not depend on β , being mainly related to the available moisture. This is a third funda-mental parameter to be accounted for. The experimental design must therefore include DSC runs performed with samples of a different moisture content, and a third axis can be added to the TTT diagram to account for it.

In the three - dimensional TTT diagram, the iso - α loci are surfaces. For a more practical approach, samples can be partially dehydrated by

5140

120

100

80

60

40

20

00 50 100 150

t / min

200 250

2 1 0.5

0.5

0.8

0.60.4

0.20.1T

/ ˚C

Figure 11.10. Iso - moisture (excess water) TTT plane relevant to starch gelatinization in bread dough samples. The dotted straight lines correspond to different heating temperatures (0.5 ° , 1 ° , 2 ° , 5 ° C/min), while the data points are the drawn from the α - versus - T plots to evidence the attainment of a given α level (values reported at the right - hand side). The tie lines connecting these points are the iso - α curves. Modifi ed from Fessas and Schiraldi (Fessas and Schiraldi 2000 ).

Figure 11.9. Extent of starch gelatinization, α , on increasing temperature, according to the DSC data recorded at various heating rates, β . Iso - α temperatures have to be reported in the TTT diagram.

280


heating them in open DSC pans within the furnace of the instrument at a given heating rate. The run has to be stopped at a temperature, T i , that can represent a rough average of the real thermal history experi-enced by the product. The DSC sample pan is then quickly cooled to room temperature, sealed, and again heated at the same rate to evaluate the residual starch gelatinization. The relevant moisture content of the sample at T i (and in the second DSC run with sealed pans) can be eventually determined as the mass loss after an overnight rest at 105 ° C after piercing the cover of the sample pan with a needle (Figure 11.11 ). This experiment allows determination of the fraction of starch gelati-nization that occurred during the fi rst (open pans at variable moisture content) and the second phase (sealed pans at constant moisture content) (Fessas and Schiraldi 2000 ). An ideal experiment should in principle be performed with a TGA - DSC combined instrument. Unfortunately, this cannot be a realistic choice, since the vaporization enthalpy of water (2.3 kJg − 1 ) is 2 orders of magnitude larger than the enthalpy of starch gelatinization. A superposition of separate investigations can nonetheless be performed (see Figure 11.7 ).

The corresponding TTT diagram (Figure 11.12 ) is indeed a curved section of the three - dimensional plot. On the same diagram, any thermal history actually experienced by the system, as well as the

Figure 11.11. Bread dough samples: starch gelatinization extent achieved in DSC open pans. Modifi ed from Fessas and Schiraldi (Fessas and Schiraldi 2000 ).


120

100

80

thermal histroy

0.70.6a

0.50.4

0.30.2

0.1

60

40

200 5 10 15

t / min

T / °

C

20 25 30

Figure 11.12. TTT diagram corresponding to the data reported in Figure 11.11 . The fi gure is a projection of a surface of the three - dimensional TTT (moisture - vs. - temperature - vs. - time) diagram.

moisture loss that occurred in the real process, can be represented with a process path. The corresponding curve crosses the iso - α surfaces (Figure 11.13 ).

The highest attained α may not regress and can indeed be referred to as the maximum progress of starch gelatinization achievable at the end of the thermal history considered. Obviously, one must take into account that real systems have a much larger mass than a DSC sample and therefore experience temperature and moisture gradients on cooking or baking. For a more detailed simulation of the process, one therefore must fi rst record the thermal history and the moisture changes in each region of the real system and then draw the relevant informa-tion about the extent of the starch gelatinization achieved.

Another practical use of the information drawn from TA investiga-tions concerns the effects of mechanical treatment, such as kneading, and layering, that affect the tightness of the gluten network of wheat - based products. Mechanical stresses squeeze some water out of the gluten phase, thus allowing protein moieties to come closer to one another and, possibly, to form direct links (mainly hydrogen bonds [Belton 1999 ; Belton 2005 ]). If the product is baked just after the experience of such mechanical stresses, more water evaporates on baking, and the fi nal product is harder and brittle. This occurs in bis-


cuits (Piazza and Schiraldi 1997 ). The experimental evidence related to this effect is provided by the DTG trace of the dough. The high T “ gluten peak ” of a stressed (overmixed) dough is smaller and closer to the large low T peak (Fessas and Schiraldi 2005 ). This indeed means that some water migrates from the gluten phase toward the neighboring starch - rich regions (these are short - range displacements), where it can evaporate more easily. A couple of hours rest is, however, enough to restore the previous water partition (Fessas and Schiraldi 2005 ). The “ relaxed ” dough leads to a softer baked product (Piazza and Schiraldi 1997 ).

The DTG trace can be of help also in predicting the effect of extra ingredients on the fi nal properties of a given product. The effect of the nonstarch carbohydrates acting as hydrocolloid sinks of moisture appears in the DTG traces with a downward shift of the high T peak. The main difference from the effects related to mechanical stresses is that this peak corresponds to more than 20% of the overall dough moisture. This means that the water fractions bound to hydrocolloids and gluten, respectively, have similar fugacities, both being less free

Figure 11.13. Sketched representation of the real thermal history experienced by the product (sigmoid curve) that crosses the iso - α surfaces in the TTT diagram. The fi gure is a projection of a surface of the three - dimensional TTT (moisture - vs. - temperature - vs. - time) diagram. Modifi ed from Fessas and Schiraldi (Fessas and Schiraldi 2000 ).


than the other water fractions of the dough. Some visual evidence of this picture is provided by light microscopy investigations (Autio and Salmenkallio-Marttila 2001 ; Autio and Laurikainen 1997 ) that show islets of nonstarch hydrocolloids dispersed within the gluten meshes, apparently hindering the contacts between them and preventing the formation of a tight network. If this situation is not perturbed by mechanical stresses, the water loss during baking is smaller than for a dough with no extra hydrocolloids. The result is a product with broader crumb alveoli (Fessas and Schiraldi 1998 ). The nonstarch hydrocolloid “ water sinks ” keep the fi nal product softer for a longer period, thus acting as antistaling ingredients (Fessas and Schiraldi 2001a ). One may defi ne an “ optimal ” shift of the DTG peak that corresponds to the desired tightness of the fi nal network by adjusting the amounts of extra hydrocolloids added to the recipe.

The case of bread prepared from a blend of wheat and buckwheat fl ours can be a suitable example (Fessas et al. 2008 ), where the non-starch carbohydrates were those water - extracted from the buckwheat hull in a separate process and added as an aqueous solution to the dough. The bread prepared from a blend of wheat and dehulled buck-wheat had a crumb with alveolar distribution quite similar to that of the crumb of the bread obtained from wheat fl our (Figure 11.14 ), although with a 45% greater density.

In this modifi ed dough, the effects of soluble nonstarch poly-saccharides has been tuned through the counterbalancing action of

A B

Figure 11.14. Pictures of the alveolar crumb structure of bread from wheat (A) and a wheat - buckwheat blend added with soluble polysaccharides (B).


globular proteins that play the role of surfactants that stabilize the dough matrix/air interface (Fessas and Schiraldi 1998 ).

Conclusions

Thermal analysis is very suitable for monitoring the transformations that take place in cereal - based food products. It requires, however, specifi c training for operators because the records obtained from a food sample usually have to be mathematically treated to unveil the single contributions coming from largely overlapped events relevant to dif-ferent components or phases of the system.

Combination with other experimental techniques is nonetheless of great help, especially when water partition and displacements are involved in the changes induced by the process.

Specifi c applications to simulate thermal treatments and adjust the recipe of a given product can be drawn from either DSC or TGA data.

References

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Belton , P.S. 1999 . On the elasticity of wheat gluten . J Cereal Sci , 29 : 103 – 107 . Belton , P.S. 2005 . New approaches to study the molecular basis of the mechanical

properties of gluten . J Cereal Sci , 41 : 203 – 211 . Biliaderis , C.G. , Page , C.M. , Maurice , T.J. , and Juliano , B.O. 1986 . Thermal charac-

terization of rice starches: A polymeric approach to phase transitions of granular starch . J Agric Food Chem , 34 : 6 – 14 .

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Courtin , C.M. and Delcour , J.A. 1998 . Wheat - Derived Arabinoxylans . J Agric Food Chem , 46 : 4066 – 4073 .

Eliasson , A.C. 1994 . Interactions between starch and lipids studied by DSC . Thermochim Acta , 246 : 343 – 356 .

Fessas , D. and Schiraldi , A. 1998 . Texture and staling of wheat bread crumb: Effects of water extractable proteins and “ pentosans . ” Thermochim Acta , 323 : 17 – 26 .


Fessas , D. and Schiraldi , A. 2000 . Starch gelatinization kinetics in bread dough: DSC investigations on “ simulated ” baking processes . J Therm Anal Calorim , 61 : 411 – 423 .

Fessas , D. and Schiraldi , A. 2001a . Water properties in wheat fl our dough I: Classical thermogravimetry approach . Food Chem , 72 : 237 – 244 .

Fessas , D. and Schiraldi , A. 2001b . Phase diagrams of arabinoxylan - water binaries . Thermochim Acta , 6518 : 1 – 7 .

Fessas , D. and Schiraldi , A. 2005 . Water properties in wheat fl our dough II: Classical and Knudsen thermogravimetry approach . Food Chem , 90 : 61 – 68 .

Fessas , D. , Signorelli , M. , Pagani , A. , Mariotti , M. , Iametti , S. , and Schiraldi , A. 2008 . Guidelines for buckwheat enriched bread: Thermal analysis approach . J Therm Anal Calorim , 91 : 9 – 16 .

Goff , H.D. , Montoya , K. , and Sahagian , M.E. 2002 . The effect of microstructure on the complex glass transition occurring in frozen sucrose model systems and foods . In: Amorphous Food and Pharmaceutical Systems . H. Levine , editor, pp. 145 – 157 . The Royal Society of Chemistry : University of Durham, UK .

Hills , B. 1998 . Magnetic Resonance Imaging in Food Science . J.Wiley & Sons : New York .

Huang , Y. , Tang , J. , Swanson , B.G. , Cavinato , A.G. , Lin , M. , and Rasco , B.A. 2003 . Near infrared spectroscopy: A new tool for studying physical and chemical proper-ties of polysaccharide gels . Carbohyd Polymers , 53 : 281 – 288 .

Kalichevsky , M.T. and Ring , S.G. 1987 . Carbohydr Res , 162 : 323 – 328 . Kontogiorgos , V. and Goff , H.D. 2006 . Calorimetric and microstructural investigation

of frozen hydrated gluten . Food Biophys , 1 : 202 – 215 . Laaksonen , T.J. and Roos , Y.H. 2000 . Thermal, dynamic - mechanical, and dielectric

analysis of phase and state transitions of frozen wheat doughs . J Cereal Sci , 32 : 281 – 292 .

Le Bail , P. , et al. 1999 . Monitoring the crystallization of amylose - lipid complexes during maize starch melting by synchrotron X - ray diffraction . Biopolymers , 50 : 99 – 110 .

Liu , Y. and Shi , Y.C. 2006 . Phase and state transitions in granular starches studied by dynamic differential scanning calorimetry . Starch/Staerke , 58 : 433 – 442 .

Lopes - Da - Silva , J.A. , Santos , D.M.J. , Freitascarla - Brites , A. , and Gil , A.M. 2007 . Rheological and nuclear magnetic resonance (NMR) study of the hydration and heating of undeveloped wheat doughs . J Agric Food Chem , 55 : 5636 – 5644 .

Morgan , K.R. , Fourneaux , R.H. , and Stanley , R.A. , 1992 . Observation by solid - state 3C CP MAS NMR spectroscopy of the transformations of wheat starch associated with the making and staling of bread . Carbohydr Res , 235 : 15 – 22 .

Noel , T.R. and Ring , S.G. 1992 . A study of the heat capacity of starch/water mixtures . Carbohydr Res , 227 : 203 – 213 .

Osborne , T.B. 1924 . The Vegetable Proteins . Longmans Greens : London . Piazza , L. and Schiraldi , A. 1997 . Correlation between fracture of semi - sweet hard

biscuits and dough viscoelastic properties . J Text Stud , 28 : 523 – 541 . Riva , M. , Fessas , D. , and Schiraldi , A. 2000 . Starch retrogradation in cooked pasta

and rice . Cereal Chem , 77 : 433 – 438 .


Riva , M. , Piazza , L. , and Schiraldi , A. , 1991 . Starch gelatinization in pasta cooking: Differential fl ux calorimetry investigations . Cereal Chem , 68 : 622 – 627 .

Riva , M. , Schiraldi , A. , and Piazza , L. 1994 . Characterization of rice cooking: Isothermal and differential scanning calorimetry investigations . Thermochim Acta , 246 : 317 – 328 .

Roduit , B. 2000 . Computation aspects of kinetic analysis . Thermochim Acta , 355 : 171 – 180 .

Roos , Y.H. 1995 . Phase Transitions in Foods . Academic Press : New York . Schiraldi , A. 2003 . Phenomenological kinetics: An alternative approach . J Therm

Anal Calorim , 72 : 885 – 900 . Schiraldi , A. and Fessas , D. 2003 . Classical and Knudsen thermogravimetry to check

states and displacements of water in food systems . J Therm Anal Calorim , 71 : 221 – 231 .

Schiraldi , A. , Piazza , L. , and Riva , M. , 1996 . Bread staling: A calorimetric approach . Cereal Chem , 73 : 32 – 39 .

Schwartzberg , H.G. 1976 . Effective heat capacities for the freezing and thawing of food . J Food Sci , 41 : 152 – 156 .

Slade , L. and Levine , H. 1991 . Beyond water activity: Recent advances based on an alternative approach to the assessment of food quality and safety . CRC Crit Rev Food Sci Nutr , 30 : 115 – 359 .

Tolstoguzov , V.B. 2003 . Some thermodynamic considerations in food formulation . Food Hydrocolloids , 17 : 1 – 23 .

Vittadini , E. , Dickinson , L.C. , Lavoie , J.P. , Pham , X. , and Chinachoti , P. 2003 . Water mobility in multicomponent model media as studied by 2 H and 17 O NMR . J Agric Food Chem , 51 : 1647 – 1652 .

Vodovotz , Y. , Hallberg , L. , and Chinachoti , P. 1996 . Effect of aging and drying on thermomechanical properties of white bread as characterized by Dymanic Mechanical Analysis (DMA) and Differential Scanning Calorimetry (DSC) . Cereal Chem , 73 : 264 – 270 .

Wasserman , L.A. , Signorelli , M. , Schiraldi , A. , Yuryev , V. , Boggini , G. , Bertiniand , S. , and Fessas , D. 2007 . Preparation of wheat resistant starch: Treatment of gels and DSC characterization . J Therm Anal Calorim , 87 : 153 – 157 .

Yuryev , V.P. , Krivandin , A.V. , Kiseleva , V.I. , Wasserman , L.A. , Genkina , N.K. , Fornal , J. , Blaszczakb , W. , and Schiraldi , A. 2004 . Structural parameters of amy-lopectin clusters and semi - crystalline growth rings in wheat starches with different amylose content . Carbohydrate Res , 339 : 2683 – 2691 .

Zobel , H.F. 1988 . Starch crystal transformations and their industrial importance . Starch/Staerke , 40 : 44 – 50 .

Chapter 12

Importance of Calorimetry in Understanding Food Dehydration and Stability

Yrj ö H. Roos

289

Introduction 289 Phase and State Transitions of Food Components 292 Calorimetric Glass Transition Measurement 293 Dielectric and Mechanical Relaxations 297 Thermal Analysis in Characterization of Food

Systems 298 The Frozen State of Foods Systems 301 State Diagrams and Dehydration 303

Spray - Drying 305 Freeze - Drying 306

Glass Transition and Stability of Dehydrated Materials 307 Conclusions 308 References 309

Introduction

Dehydration involves removal of solvent water from dissolved and hydrated food components. The process requires heat for evaporation or sublimation of water and concentration of food solids at high levels. This results in water removal to an almost anhydrous state of food components. Traditional dehydration processes are based on empirical knowledge of food material properties and processing needs to achieve desired product characteristics. Advanced dehydration processes, such


as spray - drying and freeze - drying require more a fundamental understanding of water or ice properties and their removal as well as knowledge of physicochemical properties of the dehydrated solids (Roos 1995, 2002a, 2004 ). Thermal analytical and calorimetric measurements can often be used to characterize phase transitions of food solids and water (2002b). These measurements provide data that can be used to adjust dehydration conditions and temperatures to improve dehydration processes and product characteristics, such as fl avor retention, storage stability, and fl owability of powders (Roos 1995, 2004 ).

Various phase and state transitions occur in food dehydration and storage of dehydrated foods. Phase transitions typically include evaporation of water and crystallization of food components (precrys-tallization before dehydration, crystallization during dehydration, and crystallization during storage). Most dehydrated materials, however, contain noncrystalline, amorphous solids. These can exist as solid glasses or viscous, supercooled fl uids (White and Cakebread 1966 ; Slade and Levine 1995 ; Roos 1995, 2004 ). The glassy state of materials refers to the nonequilibrium, solid state, which is universal of all glass - forming materials, such as inorganic glasses, and synthetic noncrystalline polymers, sugars, and proteins as the main amorphous food components. Typical characteristics of the glassy state include transparency, solid appearance, and brittleness (White and Cakebread 1966 ; Sperling 1992 ). In noncrystalline, amorphous systems, mole-cules have no ordered structure, and the volume of the system is larger than that of the equilibrium crystalline systems with the same composition. Amorphous, noncrystalline systems can exist as glassy solids or supercooled liquids (rubber, leather, syrup) (Slade and Levine 1991 ; Roos 1995 , 2004 ; Slade and Levine 1995 ), depend-ing on their physical state, that is, apparent solid or liquid - like properties.

The transition in which a solid, glassy material undergoes a change to a supercooled liquid is a change in state of the material rather than a change in phase (Sperling 1992 ; Roos 1995 ). This state transition is universally referred to as glass transition. Glass transition involves a change in heat capacity, which can be measured by calorimetric methods. As the glass transition is a change in the state of the system, it also results in a dramatic change in mechanical properties (Roos 1995 ). Changes in state and fl ow properties in food systems greatly

Importance of Calorimetry in Understanding Food 291

affect their behavior in dehydration equipment and storage stability at low water contents. Glass transitions of amorphous components in foods are often monitored by differential scanning calorimetry and recorded from a step change in heat capacity (Roos 2002b ). Understanding the physical state of food materials requires that pro-perties of individual food components and their interactions with each other are well characterized.

The fi rst studies referring to glass formation by food components were those of dairy powders and glucose. It was recognized that sugars formed solid, noncrystalline structures (glasses) and that the properties of noncrystalline lactose in dairy powders and ice cream were often responsible for dramatic changes in product quality (White and Cakebread 1966 ). Slade and Levine (1991, 1995) , Karel et al. (1994) , and Roos (1995, 2004) emphasized that solid, dehydrated food systems, as well as frozen food systems, contain noncrystalline (amorphous) components and that the physical state of the components controls food properties and stability. For example, water can be removed from milk by dehydration or freezing. These processes remove solvent water, and the solute molecules often remain in a disordered, dissolved or dis-persed “ amorphous ” state. Macroscopic observations of such systems have suggested that dehydration may result in glass formation. Calorimetric and other systematic studies are then required for full characterization of the glass - forming components and their properties in the dehydrated food systems.

Carbohydrates and some proteins are the most typical hydrophilic components of food solids. These components may form amorphous, noncrystalline structures at low water contents (White and Cakebread 1966 ; Slade et al. 1991 ; Roos 1995, 2004 ). The most typical food processes resulting in glass formation by amorphous or partially amorphous food components include baking, extrusion, dehydration, and freezing (Roos 1995 ). Noncrystalline food solids are extremely sensitive to water and may show various time - dependent changes that result in a dramatic decrease in food quality. The most important quality - controlling parameter of amorphous food solids is their glass transition. Structural relaxations associated with increased molecular mobility in the vicinity of the glass transition are observed from rapid changes in food properties above the glass transition. The glass transi-tion describes a temperature range over which a change of a solid glass to a softened material takes place, with the concomitant appearance


of vibrational and translational mobility of component molecules (Sperling 1992 ).

There are several glass transition - related changes in foods that affect their properties and stability. These include stickiness and caking of powders and sugar - containing products; collapse in freeze - drying and collapse of dehydrated structures; crispness of snack foods and break-fast cereals; crystallization of amorphous sugars; recrystallization of gelatinized starch; ice formation and recrystallization in frozen foods; and to some extent, nonenzymatic browning and enzymatic reactions (Roos 1995 ; Slade and Levine 1995 ; Roudaut et al. 2004 ). The objec-tive of the present review is to highlight important properties of food components associated with their thermal behavior and the use of calo-rimetry and other thermal analytical techniques in the characterization of food systems, particularly with regard to their dehydration proper-ties and stability control of dehydrated food systems.

Phase and State Transitions of Food Components

Phase transitions indicate changes in the equilibrium state of materials, and they can be classifi ed according to changes in thermodynamic properties (Roos 1995 ). The main requirement for any phase to coexist with another phase is that the Gibbs free energy of two or more phases at the transition pressure and temperature is the same. The equilibrium state is always that with the lowest Gibbs free energy. According to the thermodynamic classifi cation of phase transitions, fi rst - order phase transitions are those at which the fi rst derivatives of the thermodynamic functions exhibit discontinuity; that is, at a fi rst - order transition tem-perature there is a discontinuity in heat capacity and thermal expansion coeffi cient (Roos 1995 ). Such discontinuity occurs in melting/crystal-lization and boiling/condensation temperatures. A second - order phase transition shows a step change in heat capacity and thermal expansion coeffi cient.

Glass transitions occur in thermodynamically nonequilibrium systems, and therefore they do not involve a thermodynamic change in phase. A glass transition may be considered as a “ state transition ” of an amorphous material with some of the thermodynamic character-istics of a second - order phase transition. The amorphous state, however, is a nonequilibrium state, and its properties are time - dependent. For


example, molecules in an amorphous material can have an infi nite number of intermolecular arrangements; that is, amorphous materials have similar molecular disorder to that of liquids and gases, and any observed characteristics may be specifi c to the material only at the time of observation. Changes in amorphous materials may be followed as a function of time, and the rates of changes are likely to depend on rates of molecular relaxations and diffusion within the amorphous state. In food dehydration processes, depending on the rate of solvent (water) removal or cooling of the amorphous food solids into the solid glassy structures, different characteristics of the glassy state of the same material can be obtained. Various states of materials, their phase and state transitions, and glass formation in dehydration processes are described in Figure 12.1 .

Calorimetric Glass Transition Measurement

The glass transition is a change in state associated with a considerable change in molecular mobility. Molecular mobility is time - dependent and no exact glass transition temperatures can be measured or defi ned.

Equilibrium

Solid

Nonequilibrium

Solid

Equilibrium

Liquid

Equilibrium

Liquid

Calorimetric

SOLUTION

GLASSCRYSTAL

Dehydration

MELT

Rap

id C

ooling

Slo

wCool

ing

RUBBERCrystallization

Cooling

Heating

Co

oli

ng H

eatin

g

Solu

biliza

tion

Saturation P

last

iciz

atio

n

Heating

Heating

(Pressure)

Measurements

Figure 12.1. Equilibrium and nonequilibrium states of materials. Materials suffer state and phase transitions in various food - processing and storage conditions, which include equilibrium phase transitions such as crystallization and melting and nonequi-librium state transitions as a result of changes in temperature, water content, or both.


Observed changes in heat capacity and characteristic changes around the glass transition occur over a temperature range. The glass transition temperature is often the onset temperature of the glass transition tem-perature range (onset T g ) or the temperature corresponding to a 50% heat capacity change over the transition (midpoint T g ) as measured by differential scanning calorimetry (DSC; Figure 12.2 ).

Glass transitions have been reported for a wide range of food com-ponents, including sugars and other carbohydrates as well as proteins (Slade and Levine 1995 ; Roos 1995 ). Pure food components often show a single, clear glass transition in DSC thermograms about 100 ° to 150 ° C below their equilibrium melting temperature, T m . Calorimetric techniques measure the change in heat capacity associated with glass transition. This can be observed in heating or cooling of the material, as the glass transition is reversible. An increase in heat capacity occurs when a supercooled liquid is heated over its glass transition. The tem-perature range of the glass transitions is highly dependent on food composition and molecular weight of components. Low - molecular - weight components, for example, water and simple sugars, show glass

Exoth

erm

al H

eat F

low

T

Exotherm

Endotherm

ΔCp

Time-dependentchanges

Onset

?Midpoint

Endset

Figure 12.2. Schematic representation of typical DSC curves obtained for amor-phous materials in heating over their glass transition temperature range. The glass transition temperature, T g , is often taken from the onset temperature of the glass transition or as the midpoint value corresponding to 50% change in heat capacity occurring over the glass transition. Glass transition may involve an exotherm or an endotherm corresponding to differences in glass formation (heating/cooling rates; solvent removal/sorption rates).


transition over a relatively narrow temperature range followed by rapidly increasing fl ow above the glass transition (Roos 1993 ). High - molecular - weight food components, such as proteins and starch, as well as heterogeneous food systems often show glass transition over a wide temperature range (Hoseney et al. 1986 ; Roos 1995 ; Slade and Levine 1995 ; Ronda and Roos 2008 ).

The glass transition of systems with two or more components is dependent on component properties and their miscibility. Solvents, such as water in foods, often have a low molecular weight, and they are fully miscible with their glass - forming solutes. The glass transition temperature of a solute is highly dependent on the presence and con-centration of solvents. Even very small amounts of solvent may sub-stantially decrease the observed glass transition temperature. It has been found that an increasing amount of water decreases both the glass transition temperature and its temperature range, but it also increases the change in heat capacity of the transition (Roos and Karel 1991a, 1991b ). Other mixtures of miscible components, for example, sugars, also show composition - dependent glass transition temperatures, and they are often substantially affected by the lower - molecular - weight components (Roos 1995 ). This can also be observed in edible fi lms plasticized by other plasticizers, such as glycerol and sorbitol (Talja et al. 2007 ). In food systems, several components (e.g., starch, pro-teins) can exist in partially amorphous states, and many of them exhibit only partial miscibility or remain immiscible, forming single or several phases within food microstructure (Kalichevsky and Blanshard 1993 ; Vega et al. 2005 ).

In food processing and storage, the glass transition occurs in both cooling and heating over the glass transition temperature range, and glass transition of food solids often takes place during removal of water in freezing and dehydration processes (Figure 12.1 ). The materials show the reversible characteristics of the glass transition and also a tendency to transform toward the equilibrium state. As described by Figure 12.1 , the glassy state is a nonequilibrium amorphous state and the glass transition is a time - dependent property. It may occur at varying temperatures at different experimental time scales (e.g., fre-quency), as shown in Figures 12.3 and 12.4 . Furthermore, depending on the rate of glass formation and possible changes occurring with time in the glassy state (aging), various relaxations may be observed over the glass transition (Figures 12.2 , 12.3 , and 12.4 ).

V

H

S

T

Crystal

Liquid

TmTg

≈100-150°C

Anomalous ChangesIn Thermodynamic

Properties Dependingon Glass Properties

Equilibrium

State

Non-equilibriumState

Glass

Supercooled

liquidΔHm

Non-equilibriumState

Figure 12.3. Thermodynamic states of materials. The equilibrium liquid state occurs at and above the equilibrium melting temperature, T m . At lower temperatures, the crystalline solid state may exist at equilibrium. Noncrystalline systems can be super-cooled liquids or they may become solidlike supercooled materials (glasses) below the glass transition temperature, T g . However, the glassy state is a nonequilibrium and time - dependent state. It may have various molecular arrangements with different enthalpy, H , entropy, S , and volume, V , states. Melting of crystals involves heat of melting, Δ H m , but the glass transition involves no latent heat of the transition.

TEMPERATURE

Tg

Storage modulus or

dielectric constant

Loss modulus or

dielectric loss

αγ and β relaxations

relaxation

ME

CH

AN

ICA

L O

R D

IEL

EC

TR

IC P

RO

PE

RT

Y

Increasing frequencyMechanical and

dielectric relaxations

Figure 12.4. Mechanical and dielectric relaxations of materials in the glassy state and around the glass transition.

296


Dielectric and Mechanical Relaxations

Dielectric analysis (DEA/DETA) and dynamic mechanical analysis (DMA/DMTA) are thermal analytical methods that allow complemen-tary characterization of amorphous food systems (Kalichevsky et al. 1992 ; Talja and Roos 2001 ; Roudaut et al. 2004 ). These techniques detect relaxations in dielectric and mechanical properties of the materi-als. In general, amorphous structures are fairly stable in the solid, glassy state (Sperling 1992 ; Slade and Levine 1995 ), and the relaxation times extend to several years or decades. At temperatures around and above the transition, the solid state is rapidly transformed to a super-cooled liquid state (viscous fl uid) with more rapid time - dependent fl ow (White and Cakebread 1966 ; Roos 1995 ; Roudaut et al. 2004 ). The change in mechanical properties is observed by DMA and detected as a change in the complex moduli of the material. These changes are referred to as α relaxations, which also appear as analogous changes in dielectric properties in a DEA analysis (Figure 12.4 ). For example, dehydrated, glassy foods have a solid and brittle behavior, whereas the materials may fl ow as syrups or become soggy above the glass transi-tion (e.g., freeze - dried foods). This change is associated with a decreas-ing modulus appearing in a DMA analysis as well as a decrease of the dielectric constant.

Mechanical and dielectric properties detect glass transition in foods by their sensitivity to relaxations and changes in modulus and dielectric properties (Kalichevsky et al. 1992 ; Sperling 1992 ; Talja and Roos 2001 ). Glass transition as such cannot be measured by DEA or DMA, but these techniques detect relaxations associated with the change in heat capacity (Figure 12.4 ). Several relaxations may appear at lower temperatures ( β and γ relaxations), and they appear as changes in storage modulus, E ′ or G ′ ; loss modulus, E ″ or G ″ ; dielectric con-stant, ε ′ ; dielectric loss constant, ε ; and mechanical and dielectric loss, tan Δ , below the glass transition (Figure 12.4 ). The α relaxation is the main relaxation associated with the glass transition. The observed relaxation temperatures are highly dependent on the frequency, f , of the applied stress or dielectric disturbance, which clearly indicate the time - dependent characteristics of noncrystalline materials under dis-turbance. Many researchers have used frequencies around 1 Hz in DMA and DEA measurements. However, it seems that frequencies indicating relaxations at the calorimetric glass transition onset


temperature occur at much lower frequencies (Talja and Roos 2001 ). These frequencies may be in the range of 0.01 – 0.1 Hz, and they indi-cate the onset of molecular mobility or viscous fl ow within the glassy material; that is, the solid characteristics are changing to liquid - like material properties.

Thermal Analysis in Characterization of Food Systems

DSC is the most universal thermal analytical technique used to detect phase and state transitions of food systems. DSC requires minimal sample preparation, and the materials studied can be hermetically sealed in sample pans for the analysis at known water contents. Amorphous or partially amorphous structures in foods are formed in food processing, particularly as the result of water removal and dehy-dration. Loss of water causes the concentration of solids to increase, for example in baking, dehydration, freezing, and extrusion, as described by Figure 12.1 . Depending on the rate of solvent removal or cooling into the solid state, glasses with different properties can be obtained (Figure 12.2 and 12.3 ). These food processes form concen-trated, supercooled, amorphous, nonequilibrium materials that exhibit time - dependent changes. The materials exhibit a thermodynamic driving force toward an equilibrium state, for example, the crystalline state. This is typically observed in a DSC scan of pure food compo-nents, such as lactose and sucrose, which show a glass transition fol-lowed by a crystallization exotherm. Crystallization is time - dependent, but pure substances often show instant crystallization at a scanning rate - dependent temperature (Figure 12.5 ). The glass transition occurs over a temperature range, although it is often referred to with a single temperature value (Figure 12.3 ). Glass transition may be present in either low - moisture and dehydrated foods or frozen foods in which a concentrated solute phase is formed because water is separated as a crystalline ice phase within the material. Several real food systems and components may exist as only partially amorphous materials, and many food components are only partially miscible or immiscible forming single or several phases within food microstructure; for example, carbohydrate, protein or lipid - rich regions or phases.

Glass transition occurs in both cooling and heating and also in removal or sorption of a plasticizer or a solvent or both. The main


plasticizer of amorphous food solids is water. Water is a plasticizer to most carbohydrates and proteins. Lipids exist in separate, hydrophobic phases and show little interactions with hydrophilic components and functional groups of food polymers (Roos 1995 ). Water softens food solids by decreasing their glass transition temperature toward that of water, at around − 135 ° C. The presence and interaction of water mol-ecules with food solids results in changes in the amorphous structure. The glass transition of dehydrated food solids decreases as a result of water sorption (water uptake from surroundings), and their properties may change from those of the glassy solid to viscous liquids or syrup (sugar systems) or leathery material (protein systems) in an isothermal water sorption process.

The glass transition of amorphous sugars occurs over a temperature range of 10 ° – 20 ° C, whereas the glass transition of food polymers may extend over a temperature range of more than 50 ° C (Hoseney et al. 1986 ; Roos 1995 ; Roudaut 2004 ). The change in heat capacity ( Δ C p ) of sugar glasses around their glass transition is around 0.5 – 1.0 J/g ° C, and the transition occurs over a temperature range of about 10 ° – 20 ° C (Roos 1993 ). The Δ C p of proteins and starch is often quite small, and the glass transition may occur over a broad temperature range. The magnitude of the glass transition often increases with increasing water content, and the transition occurs over a more narrow temperature

Glass transition

Crystallization

Melting

TEMPERATURE

EN

DO

TH

ER

MA

L HE

AT

FLO

W

Tg

Tcr

Tm

Figure 12.5. Schematic representation of DSC curve typical of amorphous sugars with glass transition, T g , and crystallization, T cr , of the amorphous phase and melting of the crystals at T m .


range. The glass transition of proteins and polysaccharides may only be measurable by DSC with a relatively large level of water plasticiza-tion (Roos 1995 ). Food systems also may exhibit numerous glass transitions depending on composition and extent of phase separation (miscibility). Some anhydrous food components, for example several monosaccharides and polyols, have their anhydrous glass transitions below room temperature, and they cannot be dehydrated to solid mate-rials (Roos 1993, 1995 ). Many carbohydrates and proteins as well as other polymeric food components have glass transitions in dry states above 200 ° C, which approaches their decomposition temperature. A common problem in observing glass transitions in food systems at intermediate water content is that liquid - crystalline transitions of lipids often overlap the glass transition of hydrophilic food solids.

The effect of water on the glass transition can be predicted, for example, using the Gordon - Taylor equation (Gordon and Taylor 1952 ). We have combined the water sorption data and glass transition data to establish diagrams showing critical values for water content and water activity that result in glass transition at the storage temperature (Figure 12.6 ). Such diagrams can be established using, for example, the Gordon - Taylor equation to model water plasticization and the

-80

-60

-40

-20

0

20

40

60

80

100

120

0 10 20 30 40

WATER CONTENT (g/100g dry solids)

TE

MP

ER

AT

UR

E (

°C)

0

0.2

0.4

0.6

0.8

WA

TE

R A

CT

IVIT

Y

T g

ExtrapolatedGAB Sorption Isotherm

Glass Transition Region(Temperature-dependentcritical storage parameters)

Time-dependentcrystallization

Critical Water Content (25°C)

Critical Water Activity (25°C)

Figure 12.6. Glass transition and water sorption behavior of lactose. Water sorption results in lactose crystallization as the glass transition at the observation temperature is exceeded, and the water activity and water content become higher than the critical values.


Guggenheim - Anderson - De Boer (GAB) equation to model water sorp-tion (Roos 1995 ).

The Frozen State of Foods Systems

Freezing of water in food systems occurs at temperatures below the equilibrium melting temperature of water, T m . The equilibrium melting temperature refers to the temperature at which the last ice crystals melt during heating of a frozen material. The equilibrium melting tempera-ture is dependent on dissolved food components and their concentra-tion. Below T m , freezing of water may continue until an equilibrium amount of ice appears at the freezing temperature or a kinetically limited maximum amount of ice has formed at a lower temperature (Roos and Karel 1991c , Roos 1995 ). A maximally freeze - concentrated system shows an initial solute concentration - independent glass transi-tion temperature, T g ′ , of solutes plasticized by the unfrozen water. The unfrozen water is a continuous phase, with dispersed ice crystals that exhibit an onset of ice melting during heating at an initial concentra-tion - independent temperature, T m ′ . This behavior has been well estab-lished for many common sugars and carbohydrates (Goff 1995 ; Roos 1995 ; Slade and Levine 1995 ; Singh and Roos 2005 ). Typically, the solute concentration of a glassy unfrozen solute phase dispersing the maximum amount of ice formed in a frozen system is around 80% (w/w) (Slade and Levine 1991 ; Roos 1993 ; Talja and Roos 2001 ; Singh and Roos 2005 ). These transitions can be described by DSC curves of nonannealed and annealed systems (Figure 12.7 ). The kinetic limita-tions for ice formation and time - dependent characteristics of maximum freeze concentration can be related to the limited diffusion, high vis-cosity, and longer relaxation times as the glassy state of the unfrozen solids - unfrozen water phase is approached (Figure 12.8 ).

Phase and state transitions of maximally freeze - concentrated materi-als are complex and show the nonequilibrium nature of ice formation in calorimetric and thermal analytical studies. However, the same thermal analytical techniques, as well as electron spin resonance (ESR) and nuclear magnetic resonance (NMR) techniques, provide informa-tion about transitions of freeze - concentrated solids and ice melting (Slade and Levine 1995 ; Roos 1995 ; Laaksonen et al. 2002 ; Roudaut et al. 2004 ). Low - molecular - weight components, such as sugars, often


exhibit clearly observable and separate but time - dependent transitions (Roos and Karel 1991b,c ). In DSC measurements, the glass transition temperature of the maximally freeze - concentrated solute, T g ′ , must be taken from the onset temperature of the transition temperature range. The midpoint or endpoint of the transition cannot be defi ned because the glass transition is often not complete prior to the ice - melting transi-tion (Roos 1995 ). Hence, the Δ C p of the glass transition may remain unknown or a value that is too low may be obtained because the glass transition may not be complete when the fi rst ice crystals in the frozen system melt. Melting of ice gives a relatively sharp endothermic peak, and its onset temperature can be taken as the onset of ice melting, T m ′ , for ice within the maximally freeze - concentrated system (Roos and Karel 1991c ). As described in Figure 12.7 , freezing to this maximally freeze - concentrated state may require an isothermal treatment (anneal-

Initial Tg

T'

T'

g

m

EN

DO

TH

ER

MA

L HE

AT

FLO

W

t

t

t

t

0

1

2

3

Annealing temperature

TEMPERATURE

Tm

Annealing timeUnfrozen state

Partial freeze-concentration

Maximal freeze-concentration

Figure 12.7. A schematic representation of time - dependent ice formation in freeze - concentrated food systems. A rapidly cooled system shows a glass transition corre-sponding to the solute - water ratio of the solution when no ice is formed. Ice formation may be achieved by annealing (isothermal holding) at a temperature below T m ′ . This can be detected from an increasing glass transition temperature, T g , for the freeze - concentrated unfrozen solute phase and an increasing size of the ice - melting endo-therm. At maximum ice formation, more ice cannot be formed, and the glass transition occurs at initial water content - independent temperature, T g ′ , and is followed by onset of ice melting at T m ′ .


ing) at a temperature favoring maximum ice formation (Roos and Karel 1991c ; Singh and Roos 2005 ).

State Diagrams and Dehydration

Calorimetric transition temperatures can be shown in state diagrams (Figure 12.8 and 12.9 ). State diagrams are often used to show phase and state transition data to describe material states at various tempera-tures and levels of water plasticization (Roos and Karel 1991a ; Roos 1995 ; Slade and Levine 1991, 1995 ). A typical state diagram shows the glass transition temperature against water content with T g ′ , T m ′ , and T m data as shown for lactose in Figure 12.9 . The effect of water on the glass transition can be predicted, for example, using the Gordon - Taylor equation (Roos 1995 ), which uses the solids, water glass transition

WEIGHT FRACTION OF SOLIDS1.00

TE

MP

ER

AT

UR

E

T – Tg

40

20

0

(°C)

2.4x10

1.3x10

1.0x10

–8

–5

0

Relative

Relaxation Time

Water plasticization

Therm

al pla

sticiz

ation

Glass transition

Tm

Tm’

Tg’

Supercooled

liquid

Glassy solid

α-relaxation

Viscous flow

Maximum Ice Formation

Figure 12.8. Schematic state diagram showing the decrease in glass transition with increasing water content and decreasing relaxation times at increasing levels of thermal or water plasticization. Maximum ice formation takes place time dependently over the temperature range from the glass transition temperature of the maximally freeze - concentrated unfrozen phase, T g ′ , and onset of ice melting in the maximally freeze - concentrated unfrozen phase, T m ′ . Equilibrium ice melting occurs according to the equilibrium ice - melting temperature, T m , curve.


temperatures, and their weight fractions to predict the glass transition temperatures at various water contents. We also have used combined water sorption and glass transition data to establish diagrams showing critical values for water content and water activity. The critical water activity and water content have been defi ned as those corresponding to the glass transition occurring at a processing or storage temperature for which water sorption isotherm is shown (Figure 12.6 ). Such dia-grams can be obtained, for example, by using the Gordon - Taylor equa-tion to model water plasticization and the GAB relationship to model water sorption (Roos 1995 ). The critical water content and water activ-ity diagrams, together with state diagrams, are important tools in explaining changes in time - dependent mechanical and fl ow properties that are related to glass transition and water plasticization (Slade et al. 1991 ; Kokini et al. 1994 ; Roos 1995 ; Roos et al. 1996 ; Rahman 2006 ). Isoviscous states or relaxation time curves can be shown in state dia-grams to describe rapid changes of time - dependent characteristics of food systems above the glass transition.

0.0 0.2 0.4 0.6 0.8 1.0-150

-100

-50

0

50

100 Solubility

(equilibrium mixture

of α - and

β -lactose)Supercooled

liquid

Gla

ssEquilibrium freezing zone

Temperature range for maximumice formation

gT

gT

m

gT'

T'

gC'

Te

mp

era

ture

(°C

)

Weight Fraction of Lactose

Glass transition

range

Glass

Calorimetric

transitiontemperatures

Time-dependent

crystallization

Figure 12.9. State diagram of lactose with the glass transition temperature, T g , curve, and transition temperatures for maximally freeze - concentrated lactose solutions ( T g ′ is the glass transition temperature of a maximally freeze - concentrated solution, and T m ′ is onset temperature of ice melting in a maximally freeze - concentrated solution).


Spray - Drying

Spray - drying is an effi cient dehydration method for a large variety of liquid materials and slurries, which can be converted to small liquid droplets using rotating disks or pressure nozzle atomizers. The tiny droplets can be dehydrated in hot air within seconds, which allows continuous production of free - fl owing powders. Although the principle of the process is relatively simple, phase and state transitions of food solids have a signifi cant impact on whether the materials can be spray - dried successfully or whether the powders have free - fl owing properties in handling, packaging stages, storage, and use.

It has been suggested that formation of the glassy state from solids in spray - drying, particularly the glass - forming properties of carbohy-drates, have a correlation with spray - drying behavior of fruit juices and materials rich in sugars (Bhandari and Howes 1999 ). These materials are often extremely diffi cult to dehydrate because the solids tend to stick on drier surfaces and cake inside dehydration - and powder - handling equipment. Stickiness is probably the most important prop-erty in establishing criteria for the suitability of food materials to spray - drying. Studies of glass transitions of dehydrated sugars and high - sugar products have confi rmed that stickiness is related to the glass transition of amorphous powders (Roos and Karel 1990 ). Based on the knowledge of phase and state transitions in dehydration of liquids with dissolved substances, it may be assumed that the rapid removal of water causes vitrifi cation of the liquid droplets (i.e., solids in the droplets dehydrate and form glasslike structures) within a short time and formation of a solid particle surface (Roos 2004 ). Glass tran-sition of the solids at the surface layer of a drying droplet is a key parameter in defi ning stickiness behavior of the particles and formation of liquid bridges occurring in subsequent caking as a result of rapid decrease in surface viscosity above the glass transition. Therefore, materials with an anhydrous glass transition below room temperature cannot be spray - dried, as they cannot be converted to a solid state at room temperature.

The viscosity changes resulting from the glass transition also can be used to control agglomeration of fi ne particles and in the manufactur-ing of instant powders (Roos 1995 ). In such processes, it is essential to allow controlled stickiness on particle surfaces and adhesion of particles to form clusters. The formation of clusters is followed by


removal of water and cooling to solidify the surfaces into the glassy state. The fi nal product will have larger particles and remain free fl owing and stable at appropriate storage conditions.

Freeze - Drying

The T g ′ and T m ′ temperatures of biological materials are extremely important determinants of appropriate operation parameters in freeze - drying (Roos 2004 ). Freeze - drying requires that dissolved substances are freeze - concentrated to an almost solid state and that the highly viscous state is retained throughout the dehydration process; that is, the material should consist of solid ice and a freeze - concentrated, solid, glassy, unfrozen phase (Figure 12.10 ). Ice melting above T m ′ has a dramatic plasticization effect in a freeze - concentrated system and results in liquid fl ow. The effect of state transitions and ice melting above T m ′ are described for freeze - drying in Figure 12.10 . Accordingly, the highest allowable pressure (or ice temperature) in freeze - drying is defi ned by the initial melting temperature of ice in the system. At conditions allowing melting, fl ow may occur, and some dehydration occurs from the liquid state; the process no longer can be referred to as freeze - drying (Figure 12.10 ).

Freeze-concentratedunfrozen solute phase Ice

Freeze-driedglass solute membranes

Pores

Solid

Collapsedliquid

Flow

Freeze-dryingbelow Tm’

Freeze-drying

above Tm’

Figure 12.10. The role of onset temperature of ice melting, T m ′ , in successful freeze - drying and liquid fl ow resulting in collapse as ice temperature exceeds T m ′ in a freeze - drying process.


Loss of structure known as collapse in freeze - drying occurs above a critical temperature that allows the viscous fl ow of freeze - concen-trated amorphous solutes (Bellows and King 1973 ) as they are plasti-cized by unfrozen water (Roos 2004 ). The onset temperature of ice melting, T m ′ , determines a temperature at which a maximally freeze - concentrated system becomes plasticized by dissolving ice crystals; therefore, T m ′ can be used as a critical reference temperature for pro-duction of properly freeze - dried materials (the ice vapor pressure in freeze - drying must be kept below the ice vapor pressure at T m ′ by the control of drying pressure and heat supply for sublimation of ice). Collapse can be avoided by the use of ice temperatures below T m ′ in freeze - drying, and the T m ′ values agree well with collapse temperatures reported for freeze - drying of carbohydrate systems (Bellows and King 1973 ; To and Flink 1978 ; Roos 1995 ).

Glass Transition and Stability of Dehydrated Materials

Stickiness and caking are common problems in handling of powders containing amorphous carbohydrates. Stickiness and caking appear as the viscosity of the amorphous components decreases and powder particles adhere as they gain liquid - like fl ow properties at conditions resulting in glass transition. Glass transition of lactose may occur as a result of water plasticization in dairy powders. Such plasticization is often the cause of time - dependent lactose crystallization. An instant crystallization may be observed at a high level of rapid thermal and water plasticization (Jouppila et al. 1997 ; Haque and Roos 2005 ). A schematic representation of glass transition - related fl ow and its effect on food material behavior, including development of stickiness, caking, and crystallization, is shown in Figure 12.11 . The crystallization of lactose has been found to be highly time - dependent following the typical crystallization rate behavior of amorphous solids (Roos and Karel 1991a , Jouppila et al. 1997 ). The time - dependent lactose crystal-lization in dairy powders is often observed in water sorption studies (Haque and Roos 2005 ). These have shown that above a critical storage relative humidity, there is a loss of sorbed water (Figure 12.6 ). The loss of sorbed water in dairy powders corresponds to the difference in water sorption by amorphous and crystalline lactose. However, it should be noted that the loss of sorbed water is time - dependent, and


the crystalline form of lactose produced is dependent on the crystalliza-tion conditions (Jouppila et al. 1997 ; Haque and Roos 2005 ). Most crystals formed are anhydrous, but at the higher storage humilities increasing amounts of α - lactose monohydrate is formed. Crystallization of amorphous lactose in sealed packages and in bulk storage also results in an increase in water activity and acceleration of most deterio-rative changes, such as browning reactions and oxidation.

Conclusions

Thermal and calorimetric properties of food and biological materials at various water contents are extremely important determinants of their dehydration and stability characteristics. Calorimetric measure-ments provide data for selection of appropriate dehydration parameters and manipulation of solids composition to enhance dehydration and improve storage stability. Several dehydrated materials, particularly spray - dried and freeze - dried, exist as amorphous, glassy solids. Formation of a solid structure contributes to the success of dehydration processes and the quality characteristics of dehydrated materials. Knowledge of glass transitions and ice - melting properties of sensitive

RE

LA

XA

TIO

N T

IME

Glassy State Glass TransitionYears

Months

Days

Hours

Minutes

Seconds

Flow

Hard

enin

g, C

rackin

g

Crispness

Stability Zone Critical Zone Mobility ZoneS

tructu

ral T

ransfo

rmations

Incre

asin

g D

iffu

sio

n

TEMPERATURE, WATER ACTIVITY OR WATER CONTENT

EX

TE

NT

OF

CH

AN

GE

IN P

RO

PE

RT

Y

Fermi’s Model(M. Peleg)

‘Solid’ ‘Highly time-dependent’ ‘Instant changes’

SOLID CRITICAL ZONE

VISCOUS FLOW

LIQUID

Figure 12.11 Changes in relaxation times as a result of thermal or water plasticiza-tion in food systems. Around and above the glass transition rapidly appearing liquid - like properties of the materials result in dramatic changes in mechanical properties and diffusion. The changes in mechanical properties around glass transition may be modeled using the Fermi relationship (Peleg 1993 ).


materials is the basis of successful freeze - drying. Freeze - drying may only take place below the onset temperature of ice melting in a frozen system, as higher temperatures allow fl ow of freeze - concentrated matrices as well as collapse and loss of quality. Relationships between fl avor retention and encapsulation of volatiles and dispersed compo-nents, and formation of a glassy, continuous hydrophilic phase in dehydration processes, are important in stabilization of such compo-nents. Solids crystallization, lipid oxidation, nonenzymatic browning, and enzymatic changes are often interrelated and controlled by the glass transition and water.

References

Bellows R.J. and King C.J. 1973 . Product collapse during freeze drying of liquid foods . AIChE Symp Ser , 69 ( 132 ): 33 – 41 .

Bhandari B.R. and Howes T. 1999 . Implication of glass transition for the drying and stability of dried foods . J Food Eng , 40 : 71 – 79 .

Goff H.D. 1995 . The use of thermal analysis in the development of a better under-standing of frozen food stability . Pure Appl Chem , 67 : 1801 – 1808 .

Gordon M. and Taylor J.S. 1952 . Ideal copolymers and the second - order transitions of synthetic rubbers. I. Non - crystalline copolymers . J Appl Chem , 2 : 493 – 500 .

Haque M.K. and Roos Y.H. 2005 . Crystallization and x - ray diffraction of spray - dried and freeze - dried amorphous lactose . Carbohydr Res , 340 : 293 – 301 .

Hoseney R.C. , Zeleznak K. , and Lai C.S. 1986 . Wheat gluten: A glassy polymer . Cereal Chem , 63 : 285 – 286 .

Jouppila K. , Kansikas J. , and Roos Y.H. 1997 . Glass transition, water plasticization, and lactose crystallization in skim milk powder . J Dairy Sci , 80 : 3152 – 3160 .

Kalichevsky M.T. and Blanshard J.M.V. 1993 . The effect of fructose and water on the glass transition of amylopectin . Carbohydr Polym , 20 : 107 – 113 .

Kalichevsky M.T. , Jaroszkiewicz E.M. , Ablett S. , Blanshard J.M.V. , and Lillford P.J. 1992 . The glass transition of amylopectin measured by DSC, DMTA, and NMR . Carbohydr Polym , 18 : 77 – 88 .

Karel , M. , Anglea , S. , Buera , P. , Karmas , R. , Levi , G. , and Roos , Y. 1994 . Stability - related transitions of amorphous foods . Thermochim Acta , 246 : 249 – 269 .

Kokini J.L. , Cocero A.M. , Madeka H. , and de Graaf E. 1994 . The development of state diagrams for cereal proteins . Trends Food Sci Technol , 5 : 281 – 288 .

Laaksonen T.J. , Kuuva T. , Jouppila K. , and Roos Y.H. 2002 . Effects of arabinoxylans on thermal behavior of frozen wheat doughs as measured by DSC, DMA, and DEA . J Food Sci , 67 : 223 – 230 .

Peleg , M. 1993 . Mapping the stiffness - temperature - moisture relationship of solid biomaterials at and around their glass transition . Rheol Acta , 32 : 575 – 580 .

Rahman M.S. 2006 . State diagram of foods: Its potential use in food processing and product stability . Trends Food Sci Technol , 17 : 129 – 141 .


Ronda F. and Roos Y.H. 2008 . Gelatinization and freeze - concentration effects on recrystallization in corn and potato starch gels . Carbohydr Res , 343 : 903 – 911 .

Roos Y. 1993 . Melting and glass transitions of low molecular weight carbohydrates . Carbohydr Res , 238 : 39 – 48 .

Roos Y.H. 1995 . Phase Transitions in Foods . Academic Press : San Diego . Roos Y.H. 2002a . Importance of glass transition and water activity to spray drying

and stability of dairy powders . Le Lait , 82 : 475 – 484 . Roos Y.H. 2002b . Thermal analysis, state transitions, and food quality . J Therm Anal

Calorim , 71 : 197 – 203 . Roos Y.H. 2004 . Phase and state transitions in dehydration of biomaterials and foods .

In: Dehydration of Products of Biological Origin , A.S. Mujumdar , editor, pp. 3 – 22 . Science Publishers : Enfi eld .

Roos Y. and Karel M. 1990 . Differential scanning calorimetry study of phase transi-tions affecting the quality of dehydrated materials . Biotechnol Progr , 6 : 159 – 163 .

Roos Y. and Karel M. 1991a . Applying state diagrams to food processing and devel-opment . Food Technol , 45 , 66 , 68 – 71 , 107 .

Roos Y. and Karel M. 1991b . Nonequilibrium ice formation in carbohydrate solu-tions . Cryo - Letters , 12 : 367 – 376 .

Roos Y. and Karel M. 1991c . Amorphous state and delayed ice formation in sucrose solutions . Int J Food Sci Technol , 26 : 553 – 566 .

Roos Y.H. , Karel M. , and Kokini J.L. 1996 . Glass transitions in low moisture and frozen foods: Effects on shelf life and quality . Food Technol , 50 ( 11 ): 95 – 108 .

Roudaut G. , Simatos D. , Champion D. , Contreras - Lopez E. , and Le Meste M. 2004 . Molecular mobility around the glass transition temperature: A mini review . Innov Food Sci Emerg Technol , 5 : 127 – 134 .

Singh K.J. and Roos Y.H. 2005 . Frozen state transitions of sucrose - protein - cornstarch mixtures . J Food Sci , 70 ( 3 ):E 198 – E 204 .

Slade L. and Levine H. 1991 . Beyond water activity: Recent advances based on an alternative approach to the assessment of food quality and safety . Crit Rev Food Sci Nutr , 30 : 115 – 360 .

Slade L. and Levine H. 1995 . Glass transitions and water - food structure interactions . Adv Food Nutr Res , 38 : 103 – 269 .

Sperling L.H. 1992 . Introduction to Physical Polymer Science , 2nd edition . John Wiley & Sons : New York .

Talja R.A. and Roos Y.H. 2001 . Phase and state transition effects on dielectric, mechanical, and thermal properties of polyols . Thermochim Acta , 380 : 109 – 121 .

Talja R.A. , Hel é n H. , Roos Y.H. , and Jouppila , K. 2007 . Effect of various polyols and polyol contents on physical and mechanical properties of potato starch - based fi lms . Carbohydr Polym , 67 ( 3 ): 288 – 295 .

To , E.T. and Flink , J.M. 1978 . “ Collapse, ” a structural transition in freeze dried carbohydrates. II. Effect of solute composition . J Food Technol , 13 : 567 – 581 .

Vega C. , Kim E.H.J. , Chen X.D. , and Roos Y.H. 2005 . Solid - state characterization of spray - dried ice cream mixes . Coll Surf B: Biointerfaces , 45 : 66 – 75 .

White G.W. and Cakebread S.H. 1966 . The glassy state in certain sugar - containing food products . J Food Technol , 1 : 73 – 82 .

Chapter 13

High - Pressure Calorimetry and Transitiometry *

Stanislaw L. Randzio and Alain Le Bail

311

Introduction 311 High - Pressure Calorimetry 313 Scanning Transitiometry 317 Applications 324

Water in Pork Muscle 324 Frozen Water Ratio in Gelatine Gels 326 Pressure Shift Freezing 329 Gelatinization of Starch 330 Phase Stability of Systems Containing Lipids 336

Conclusions 337 References 338

Introduction

The discovery by Bridgman in 1914 (Bridgman 1914a ) of a pressure - induced coagulation of egg white enhanced research on the infl uence of pressure on biological and food systems. From the results obtained over the years, it became evident that the use of pressure can lead to developments of food products with new properties, such as high - pressure - processed jams prepared by nonheating food processing, which entered the market in Japan in 1992 (Hayashi 2002 ). Other important technological applications are concerned with high - pressure processing of food, especially freezing - thawing and crystallization

* M. Malecki is acknowledged for technical assistance in the preparation of this chapter.


processes (Knorr 1999 ) and the use of extruders. An understanding of the effects of processing on the physical properties of food materials should allow prediction of the formulation of raw materials and pro-cessing conditions so as to achieve desired end product properties. To achieve this goal, systematic studies must be done to develop a data-base on the physical properties of food materials as a function of variables relevant to processing (Kaletunç and Breslauer 1996 ). Calorimetry is a powerful tool for determination of thermophysical properties of matter and processes over wide ranges of external condi-tions (Randzio 1998 ; Randzio 2002 ), and thus it is a suitable tool for creating such databases. However, the calorimetric techniques, mainly differential scanning calorimetry (DSC), to date have been mostly used only to evaluate the effects of high - pressure processing, the calorimet-ric measurements being performed after processing under atmospheric pressure. For example, Stute et al. (Stute et al. 1996 ) compressed aqueous suspensions of wheat starch at 293 K at various pressures up to 500 MPa, and then after compression up to a selected pressure, the sample was decompressed, removed from the autoclave, and analyzed with a classical DSC to verify the degree of pressure - induced gelati-nization at 293 K. Douzals et al. (Douzals et al. 2001 ) have placed the autoclave in a thermostat and were able to perform similar pressure measurements over the temperature interval from 253 K to 373 K. However, the degree of gelatinization caused by the processing at various pressures and temperatures also was determined after process-ing by using classical DSC under atmospheric pressure. However, one must note that such a use of DSC can give indicate the effect of pres-sure only for irreversible phenomena. To be able to understand the real role of pressure in food processing one must know the phase diagrams of its constituents and the mechanisms of transitions between the phases or states. Phase diagrams of biomacromolecules and biopoly-mers can be extremely complicated (Smeller 2002 ), and an interplay between temperature and pressure is sometimes diffi cult to interpret. For this reason, it is important to make calorimetric measurements under typical processing conditions of high hydrostatic pressure and temperature under well - determined pressure and temperature condi-tions and to determine the thermodynamic and thermomechanic param-eters of the transitions. This chapter describes such direct high - pressure calorimetric techniques and reviews their applications in investigation of selected systems important for food science and technology.

High-Pressure Calorimetry and Transitiometry 313

High - Pressure Calorimetry

Figure 13.1 presents a schematic diagram of a high - pressure calorimet-ric system developed by LeBail et al. (Le Bail et al. 2001 ; Zhu et al. 2004 ). The high - pressure calorimetric system is composed of a dif-ferential calorimetric detector made from 220 thermocouples intercon-nected between the two calorimetric vessels, high - pressure pump, hydraulic fl uid reservoir, pressure detector (Asco Instruments, France), and a circulating liquid thermostat. The pressure in the system was controlled by proportional - integral - derivative computer software through a stepping motor and a gear box of the high - pressure pump (Nova Swiss, Switzerland). The processing of the calorimetric signal was performed with computer software. The investigated substance was always placed in a small fl exible plastic pouch. A detailed view of the calorimetric system elements can be seen in Figure 13.2 . The high - pressure vessels connected to the high - pressure hydraulic system by fl exible stainless steel capillary tubing can be easily introduced into the cavities of the differential calorimetric detector, placed in the calo-rimetric block, which in turn is surrounded with a copper coil in which

Figure 13.1. Schematic diagram of a differential high - pressure calorimeter: (1) dif-ferential calorimetric detector, (2, 3) calorimetric vessels, (4) high - pressure pump, (5) hydraulic fl uid reservoir, (6) pressure detector, (7) circulating liquid thermostat, (8), pressure control, (9) calorimetric signal processing, (10) investigated substance in a fl exible plastic pouch.


a temperature - controlled liquid circulates, ensuring a good temperature stability. The whole system is placed in a stainless steel fl ask fi lled with heat - insulating material. Figure 13.3 presents a detailed view of a high - pressure calorimetric vessel made from stainless steel. The investigated substance was sealed under vacuum in a polyethylene pouch. The high - pressure closing of the vessels was done from one side with a nitrile O - ring placed on a plug, with a threaded plug holding it in place. From the other side, the calorimetric vessels were connected to the hydraulic high - pressure system through stainless tubing (3.2 mm outside diameter) and standard Harwood connections. The internal volume of the calorimetric vessels was 4.6 cm 3 .

The pressure sensor was calibrated against a Bourdon reference pressure gauge. The calorimeter temperature was calibrated against a K - type thermocouple (Omega, USA) placed in the calorimetric vessel at selected temperatures between 253 K and 293 K. The calibration of the calorimetric detector was carried out by joule effect using a 100 - ohm resistance settled in the high - pressure vessel.

Processes investigated in this calorimeter can be induced either by pressure variations at constant temperature or by temperature varia-tions at constant pressure. Figure 13.4 presents an example of melting of ice at 265.9 K induced by pressure variations at a rate of 16.7 kPa/s (1 MPa/min). Integration of the calorimetric trace gave the value of the

Figure 13.2. Experimental setup of a high - pressure calorimeter: (1) high - pressure vessels, (2) fl exible stainless steel capillary tubing, (3) cavities of the differential calorimetric detector, (4) calorimetric block, (5) copper cooling coil.

Figure 13.3. High - pressure calorimetric experimental vessel: (1) body of the vessel made from stainless steel, (2) polyethylene pouch containing the investigated sub-stance, (3) nitrile O - ring placed on a plug, (4) threaded plug holding the closing in place.

Figure 13.4. Calorimetric trace of melting of ice at 265.9 K induced by pressure variations at a rate of 16.7 kPa/s (1 MPa/min).

315


latent heat of fusion equal to the value derived from Bridgman ’ s high - pressure volumetric data (Bridgman 1912 ) within a 3% agreement, which is a good validation of the instrument correctness. It is worth noting that the Calvet - type calorimeters are also suitable instruments for measurements performed under typical processing conditions of high hydrostatic pressure and temperature of various processes under well - determined pressure and temperature conditions that are impor-tant for food science and technology. Such instruments have been used either as a single calorimetric detector (Chourot, LeBail, and Chevalier 2000 ) or as a differential calorimetric device (Randzio, Grolier, and Quint 1994 ). In differential mounting, a Setaram C80 calorimeter was used in an upside - down position. This permitted using differential calorimetric vessels fi xed on a laboratory table and connected to the high - pressure pump and pressure detectors with rigid stainless steel tubing, allowing performance of direct calorimetric measurements up to 400 MPa. This was the fi rst pressure - controlled scanning calorimeter with linear pressure variations at rates from 0.5 kPa/s (30 kPa/min) to 0.2 MPa/s (12 MPa/min).

When performing high - pressure calorimetric measurements, one should realize that the signifi cance of the calorimetric signal depends on the use of the pressure - transmitting fl uid. If the pressure is transmit-ted to the calorimetric vessel through the substance under investigation (liquid, liquid suspension, liquid emulsion, or even a paste), the heat developed as a result of pressure variation is proportional to the coef-fi cient of thermal expansion of the substance under investigation (Randzio 1985 ). This is because the mass of the substance contained in the calorimetric vessel varies, m = V E / V , where V E is the internal volume of the calorimetric vessel and V is the molar or specifi c volume of the substance under investigation, the latter one being pressure - dependent. This is a kind of open mass vessel (quasi - constant volume), in which the substance under investigation entirely fi lls the calorimet-ric vessel and at least a part of the external tubing connecting to the pressure generator. In investigating solid samples, the pressure must be transmitted by a fl uid. Thus, the thermal effect developed due to a pressure variation is composed of two contributions. The fi rst is pro-portional to (d V /d T ) p of the solid sample under investigation and the second to the thermal expansion coeffi cient of the pressure - transmit-ting fl uid. However, an exact separation of the two contributions requires careful treatment of the data (Rodier-Renaud et al. 1996 ).


Scanning Transitiometry

Figure 13.5 presents schematically a relatively new technique called scanning transitiometry (Randzio 1996 ). The function of scanning transitiometry consists of scanning one of the three variables ( p , V , or T ) when the second is kept strictly constant. During the scanning, the variations of the dependent variables and the associated calorimetric signal are simultaneously recorded. From these two quantities and the scanned variable, two thermodynamic derivatives, thermal and mechan-ical, are simultaneously determined for the system under study. Figure 13.6 presents four thermodynamic situations covered by scanning tran-sitiometry, where from the state variables and the heat effect one can determine four pairs of thermodynamic derivatives (Randzio 1997 ). Each of the situations has specifi c applications, which prove its par-ticular utilities. In studying food systems, the most useful is the use of temperature as a scanned variable at constant pressure and the use of pressure as a scanned variable at constant temperature. In the former case, the output variables are heat capacity at constant pressure and thermal expansion. After proper integration, one obtains enthalpy and volume variations caused by the applied temperature change. In the latter case, the output variables are pressure derivative of entropy and compressibility. After proper integration, one obtains heat of transition and associated volume change. Sometimes, especially for transitions

volume 1–5 cm3

5 +10–6 –5 +10–3 cm3/s

10–7–10–1W

223–673K

0.1–5mK/s

0.1–400MPa

0.001–0.05MPa/s

temperature

pressure

heat flux

COMPUTER

Figure 13.5. Scheme of basic principles of scanning transitiometry.


with a negative slope of the equilibrium line, the use of temperature as scanned variable at constant volume is advantageous. As it is shown in Figure 13.5 during the transitiometric experiment, the experimenter can see all thermodynamic variables of the process under investigation. The screen in Figure 13.5 exhibits an isobaric investigation of thermal gelatinization of a 50% water suspension of wheat starch by scanning temperature at a rate of 2.5 mKs − 1 under 90 MPa of pressure. The fun-damental advantage of the scanning transitiometry with respect to calorimetry is that the former technique gives simultaneously two contributions to a thermodynamic potential change, thermal and mechanical, thus permitting description of a transition in a single experiment; with calorimetry, such a description requires at least a few measurements performed at various pressures, and it is not as precise.

Figure 13.7 presents a schematic diagram of a scanning transitiom-eter, which was used in investigation of pressure infl uence on the phase transformation occurring during thermal gelatinization of aqueous wheat starch suspensions (Randzio and Orlowska 2005 ). It consists of a calorimeter equipped with high - pressure vessels, a pressure - volume - temperature system, and a LabView - based virtual instrument (VI) soft-ware. Two calorimetric detectors made from 622 thermocouples each are mounted differentially and connected to a nanovolt amplifi er. The calorimetric detectors are placed in a calorimetric metallic block, the temperature of which is directly controlled with an entirely digital feedback loop of 22 - bit resolution ( ∼ 10 − 4 K), which is part of the tran-sitiometer software. The calorimetric block is surrounded by a heating - cooling shield. The temperature difference between the block and the

T = const

INPUTS OUTPUTS

P = f(t)

Thermal(∂S/∂P)T = –(∂V/∂T)P

(∂V/∂P)T

(∂S/∂P)T = (∂P/∂T)V

(∂V/∂P)T

(∂H/∂T)P

(∂V/∂T)P

(∂U/∂T)V

(∂P/∂T)V

PVT Mechanical

T = const

V = f(t)

Thermal

PVT Mechanical

P = const

T = f(t)

Thermal

PVT Mechanical

V = const

T = f(t)

Thermal

PVT Mechanical

Figure 13.6. Thermodynamic scheme of scanning transitiometry.


heating - cooling shield is set as constant (5, 10, 20, or 30 K) and using an additional controller. The temperature measurements, both absolute and differential, are performed with calibrated Pt 100 sensors. The heaters are homogeneously embedded on the outer surfaces of both the calorimetric block and the heating - cooling shield. The whole assembly is placed in a thermal insulation embedded in a stainless steel body and placed on a stand that permits moving the calorimeter up and down over the calorimetric vessels. When performing measurements near 273 K or below, dry air is pumped through the apparatus.

The calorimetric vessels are made from 0.8 - cm internal diameter 316 SS tubing and are fi xed on a mounting table attached to the mobile stand. A fl exible ampoule containing the sample is placed in the mea-suring vessel on the top of a spring, ensuring placement of the sample in the center of the calorimetric detector. Another technique is to use mercury as the hydraulic fl uid and place a sample of prepared material directly on the mercury. Mercury offers a great advantage because its compressibility is very low, which is extremely advantageous for mea-surements of both quantities of volume variations and heat fl ux. Only the measuring vessel is connected to the PV line. The reference vessel acts only as a thermal reference; a stainless steel bar of appropriate dimensions is placed in it to balance the baseline of the differential

calorimetric detector

upper entries

investigated

substance

high pressure

tubing

calorimetric

block

heat.-cool. shield

cone plug

ampoule

calorimetric detector

heat insulation

spring

dry air flow

cooling fluid

measuring vessel reference vessel

Pt 100

air

TDIFF TSECURITYTcalorimetric

signal80W 300W

computertemp.control

step motorcontrol

pump

pressuredetector

data acquisition&

process control

Figure 13.7. Schematic diagram of a scanning transitiometer.


calorimetric signal. The tubing of both measuring and reference vessels are connected to reducers placed inside the calorimeter when it is in the lowered (measuring) position. The connections from the reducers to the manifold are made with thin stainless steel capillaries to reduce heat losses to the environment. The vessels are closed, with a cone plug fi xed in place by an internally threaded cover, which also acts as a heat exchanger between the calorimetric vessel tubing and the calo-rimetric detector. Two sleeves also are fi xed on the calorimetric vessel tubing below the cover to help control the heat exchange between the calorimetric vessel tubing and both the calorimeter block and the shield.

The piston pump (9 cm 3 of total displaced volume) is driven by a stepping motor controlled by the transitiometer software (manual control is possible during preparatory operations). The pressure detec-tor is a Viatran 245 transducer, 100 MPa full range with a precision of 0.15% full scale defl ection (fsd).

The pressure detector, the output of the calorimetric amplifi er, and the stepping motor are connected to a NI PCI - MIO - 16XE - 50 multi-function board through a NI SCB - 68 shielded connector block. The temperature measurements and digital control of the calorimetric block are performed through a serial port. The software, elaborated with the use of LabView language, performs as a virtual instrument (VI). It consists of 90 subVI, each responsible for a particular function: pres-sure measurement, temperature measurement, counting the motor steps for recording the volume variations, measuring the calorimetric signal, etc., and each performs independently. However, all the subVIs form a hierarchical structure with a top window, where the experimenter can see simultaneously all four variables (pressure, P , volume variations, V , temperature, T , and heat fl ux, q ) associated with the process under investigation and the current status of the temperature and pressure control loops.

The experiments are performed by starting thermal and mechanical stabilization for at least 5000 s, then the temperature scanning starts, which is accompanied by automatic volume compensation to keep the pressure constant. At the end of the scan, the temperature is kept con-stant for at least 5000 s. Any static baseline shift of the calorimetric signal between the low and high temperature stabilizations is corrected. No corrections are made for the calorimetric signal recorded during the temperature scan.


The method presented here is rather simple and safe in practice. The total volume of the liquid phase under pressure is only about 20 ml; the energy accumulated in it is rather small and not dangerous. The mercury used as a hydraulic fl uid is always contained in a closed space. In case of a leak, the mercury is collected on a special protecting plate. The calori-metric vessels are conveniently and reproducibly closed and opened with a torque wrench with the vessels placed in a specially designed holder. Figure 13.8 a – c presents a view of transitiometric vessels: stan-dard high - pressure vessels, the vessels kept in a holder to facilitate reproducible closing and opening of the vessel by a dynamometric wrench, and a sample of a gelatinized sample of a 50% aqueous suspen-sion of wheat starch pushed out after a transitiometric experiment.

Because of the high sensitivity of the instrument, some precautions must be taken to ensure valid measurements. In the case of the calori-metric signal, the main precaution is to carefully compensate the thermal balance of the differential calorimetric vessels. It is also impor-tant to keep the initial mercury level always in the same position, just above the entry to the calorimetric detector zone. Adjustment of the mercury level is easily done with the motorized pump. With respect to the volumetric component, it is very important that displacement

Figure 13.8. Transitiometric vessels: (a) standard high - pressure vessels; (b) the vessels kept in a holder to facilitate reproducible closing and opening of the vessel by a dynamometric wrench; (c) a sample of a gelatinized sample of 50% aqueous suspension of wheat starch pushed out after a transitiometric isobaric experiment under 90 MPa.


of the mercury during calibration experiments be suffi ciently slow to avoid overpressure in the fl ow lines, and differentiation of the piston displacement must be carefully done to avoid excessive noise on the one hand and excessive damping on the other.

The temperature and energy scales of the differential calorimetric detector were calibrated under atmospheric pressure with the fusion of gallium, T m = 302.91 K, Δ H m = 5.59 kJ mol − 1 ; p - bromochloro - benzene, T m = 337.73 K, Δ H m = 18.760 kJ mol − 1 ; p - di - bromobenzene, T m = 360.45 K, Δ H m = 20.530 kJ mol − 1 ; benzoic acid, T m = 395.55 K, Δ H m = 18.062 kJ mol − 1 ; and indium, T m = 429.75 K, Δ H m = 3.28 kJ mol − 1 . The calibration experiments were done by enclosing a calibration sub-stance in a 75 - mm - long thin glass tube placed in the center of the calorimetric vessel. In order for the internal heat exchange to resemble that occurring in the real experiment, but to avoid thermal effects of gelatinization, the remaining inner space of the calorimetric vessel was fi lled with dried starch. The precision of the temperature scale is ± 0.2 K. The energetic calibration constant k c of the calorimetric detec-tor depends on temperature and is described by Equation 13.1 :

k WV T Kc− − −( ) = × + ×1 3 63 423 10 9 993 10. . (13.1)

The mean deviation between Equation 13.1 and the calibration data is 1.4%. The reproducible resolution of the calorimetric detector varies from 1.3 × 10 − 7 W at 303 K to 1.6 × 10 − 7 W at 430 K. As reported previ-ously, for properly designed experimental vessels, the energetic calibration constant of the calorimetric detector does not depend on pressure (Randzio, Grolier, and Quint 1994 ).

The volumetric calibration of the high pressure pump was performed by weighing 11 mercury samples displaced by known numbers of motor steps. Each motor step corresponded to a displacement of (5.22 ± 0.03) × 10 − 6 cm 3 .

The volumetric calibration of the high pressure pump and both energetic and temperature calibrations of the calorimetric detector were verifi ed by test measurements using isobaric fusion of benzene, for which both enthalpy and volume of the transition are exactly known (Bridgman 1914b ). Figure 13.9 presents an example of such measure-ments performed at a scanning rate of 2.5 mK s − 1 at 100 MPa. The mean results from eight independent measurements gave the following: Δ fus V (100 MPa) = 0.1038 ± 0.0028 cm 3 g − 1 [the respective literature


value is Δ fus V (100 MPa) = 0.1026 cm 3 g − 1 ]; Δ fus H (100 MPa) = 131.1 ± 2.1 J g − 1 [the respective literature value is Δ fus H (100 MPa) = 126.3 Jg − 1 ]; T fus,onset (100 MPa) = 304.2 ± 0.3 K and T fus,peak (100 MPa) = 305.7 ± 0.5 K [the literature value is T fus (100 MPa) = 305.6 K given without any specifi cation]. The agreement with volumetric data is very good. Small differences with the thermal data probably are caused by internal heat exchange conditions in the calorimetric vessel. In test measurements, only 0.4 g of benzene was fl oating on the mercury, whereas in the cali-bration experiments, the calibration substances were placed in the center of the calorimetric vessel and were surrounded by a dry starch powder.

Figure 13.10 presents results of another test performed on the tem-perature and pressure dependence of the thermal expansion of the mercury used as hydraulic liquid. The hydraulic liquid was displaced to the top of the empty calorimetric vessel and temperature - scanning measurements performed at a rate of 2.5 mK s − 1 at various pressures. The thermal expansion of the hydraulic fl uid is almost constant at a given pressure and only very slightly decreases with temperature. The mean values are as follows: 0.949 ± 0.033 mm 3 K − 1 at 10 MPa, 0.895 ± 0.038 mm 3 K − 1 at 60 MPa, and 0.888 ± 0.050 mm 3 K − 1 at 100 MPa. This test was important for analysis of transitions observed

–10

–30Endo

a

b

–50

–70

–90

–110

–130299 301 303

Temperature (K)

305 307 309

120

100

80

60

40

20

0

Heat flux (

mW

·g–

1)

dV

/dT

(m

m3

K–

1 g

–1)

Figure 13.9. Example of simultaneous transitiometric traces (heat fl ux and volume variations) of isobaric melting at 100 MPa of 0.3858 g of benzene used as a verifi ca-tion test for thermal and volumetric calibrations of the transitiometer used in the present study: (a) heat fl ux, (b) dV/dT.


in food systems as a function of temperature under isobaric conditions, which are discussed later.

Applications

Water in Pork Muscle

High pressure has a dramatic effect on the phase transition behavior of water, encompassing several forms of ice crystals, depending on the pressure and temperature (Bridgman 1912 ). This phase transition phe-nomenon offers several potential applications in food processing, such as pressure - shift freezing, high - pressure freezing, high - pressure thawing, etc. (Cheftel, Thiebaud, and Dumay 2002 ; Kalichevsky, Knorr, and Lillford 1995 ). Water is a major component of most foods, especially fresh products (e.g., meat, fi sh, vegetables, fruits), and prop-erties of such foods are strongly related to the properties and content of water. Such foods containing water are, with respect to phase transi-tion phenomena, somewhat similar to water - ice phase transitions when subjected to high - pressure and low - temperature processing (Le Bail et al. 2003 ).

Water in foods can exist either in a free and thus freezable state or in a bound and thus nonfreezable state. The content of freezable water

Figure 13.10. Volume variations during isobaric temperature scans at various pres-sures with only hydraulic liquid (mercury) present in the system. The data at 10 MPa and 100 MPa are shifted by +0.3 mm 3 K − 1 and − 0.3 mm 3 K − 1 , respectively, to avoid overlapping.


traditionally has been considered to depend on freezing temperature (Pham 1987 ). The presence of solutes in the aqueous phase shifts the phase diagram of water, lowering the freezing point or ice - melting temperature at atmospheric pressure. This effect is enhanced with increasing solute concentrations (Fennema 1973 ; Cheftel, Levy, and Dumay 2000 ). Therefore, phase transition processing of water in foods during high - pressure treatment is more complex than that of pure water. In this respect, an example of high - pressure calorimetric appli-cations in the investigation of real foods is an evaluation of the phase - transition behavior of water in pork muscle (Zhu, Ramaswamy, and Le Bail 2004 ), described below.

Small samples of fresh pork muscle specimen (0.62 – 0.72 g) were prepared and vacuum - packaged in polyethylene bags (80 - μ m - thick multiplayer fi lm). While awaiting calorimetric experiments, the pack-aged samples were stored at 277 K. To make measurements, the inves-tigated sample was placed in the calorimetric vessel and either isothermal pressure scanning or isobaric temperature scanning was performed. After calorimetric experiments, moisture content in each investigated sample was determined by drying in an oven at 376 K for 24 h.

For isothermal pressure - scanning measurements, the calorimetric temperature was set at a selected value (268, 263, 258, and 253 K). Once the calorimetric signal showed a stable baseline, the pressure was increased linearly at a rate of 5 kPa/s (0.3 MPa/min), and the heat fl ow was recorded every 5 s. When the pressure reached the corresponding phase - change temperature, the frozen sample started to melt, resulting in a peak of heat fl ow. Figure 13.11 presents a comparison of calorimet-ric heat fl ow signals of thawing of pure ice and of ice in a frozen pork muscle at 263 K induced by a linear pressure scans at a rate of 5 kPa/s (0.3 MPa/min). Figure 13.12 presents similar isothermal heat fl ow signals of pressure - induced thawing of ice in frozen pork muscle recorded at various temperatures. It is worth noting that this is a heat fl ow calorimetric detector; the temperature increase is extremely small, thus it ensures quasi - isothermal conditions for the measurements performed.

The temperature - scanning measurements were carried out at various constant pressures by increasing (for thawing) or decreasing (for freez-ing) the temperature at a prescribed rate. Figure 13.13 presents results of a typical temperature - scanning measurement of freezing of ice in a pork muscle performed at a rate of 2.5 mKs − 1 (0.15 K/min) under pres-


sure of 111.7 MPa. At the transition, the temperature scan was perturbed because of a rapid release of heat during fast crystallization of ice.

Frozen Water Ratio in Gelatine Gels

The same experimental procedure as that described above for pure water and water in pork muscle has also been used with gelatine gels containing 2% and 10% of dry gelatine (Chevalier-Lucia et al. 2003 ).

–1000.1 50 100

Pressure (MPa)150 200

–80

–60

Pork Water

–40

–20

0

Heat flux (

mW

/g)

Figure 13.11. Comparison of thawing heat fl ux of pure ice and frozen pork muscle induced by pressure scans at a rate of 5 kPa/s (0.3 MPa/min) at 263.1 K.

Pressure (MPa)

Heat flux (

mW

/g)

–40

–268.0K –263.1K –253.1K–258.0K

–30

–20

–10

0

0.1 25 50 75 100 125 150 175 200 250

Figure 13.12. Thawing heat fl ux of frozen pork muscles induced by a pressure scan at a rate of 5 kPa/s (0.3 MPa/min) at various temperatures.


Dried powdered gelatine (Merck, Darmstadt, Germany) was dissolved in distilled water and placed in an hermetic fl ask. The solution main-tained at 293 K was mixed for 1 h at 100 rpm with a magnetic stirrer. The mixture was then heated for 30 min at 323 K at the same stirring rate. The formed gelatine gel was then stored for 12 h at 277 K to allow maturation. Three samples of 5 g of gel were dried at 375 K for 24 h to check the fi nal water content in the gels before the calorimetric mea-surements. The pressure - controlled scanning calorimetric measure-ments were then performed with gels containing 2% and 10% of dry matter at three temperatures: 268, 263, and 258 K. For each tempera-ture, the onset pressure, the peak pressure, and the latent heat were measured, always for three different samples. It was observed that whatever the concentration in dry matter, the latent heat of gelatine gels decreased with the melting temperature as observed previously for pure water. As illustrated in Figure 13.14 , whatever the concentra-tion in dry matter, the latent heat of the gelatine gel decreased with the melting temperature as observed for water. The latent heat versus temperature evolution was fi tted by a second - order polynomial expres-sion given by Equations 13.2 and 13.3 for 2% and 10% gelatine gels, respectively.

L T T R22 20 0921 6 668 318 8 0 991% . . . .= ⋅ + ⋅ + = (13.2)

L T T R102 20 0884 8 922 296 9 0 999% . . . .= ⋅ + ⋅ + = (13.3)

Heat flux (

mV

)

2.5

2.0

1.5

1.0

0.5

0.0

–0.50 1200 3600

Heat flux

Temperature

Time (s)

6000

Tem

pera

ture

(K

)

259

258

257

256

Figure 13.13. Heat fl ux of crystallization of water in pork muscle induced by cooling under pressure of 111.7 MPa.


The dry matter content appeared to have an infl uence on the latent heat under pressure. The higher the dry matter, the lower was the latent heat. This phenomenon observed under atmospheric pressure was also valid under high pressures. Figure 13.15 presents the ratio of the latent heat observed for gelatine gels and that of pure water. Under atmo-spheric pressure (273 K), this ratio for the 2% and 10% gelatine gels was 0.98 and 0.9, respectively. The difference between these results and the water content of the gels is probably due to the bound water fraction. The bound water fraction, which does not freeze, corresponds to some water molecules fi xed on polar groups of components such as proteins. It can be seen in Figure 13.15 that when the melting tempera-ture decreases (the phase - change pressure increases), this ratio

Late

nt heat (J

/g)

100

200

300

400

253 258 263

Temperature (K)

268 273

Figure 13.14. Evolution of the latent heat of pure water (square, experimental data; dash, Bridgman ’ s data) and of gelatine gels (circle, 2%; diamond, 10% in dry matter) according to the melting temperatures.

Figure 13.15. Evolution of the ratio of latent heat of melting gelatine gels and latent heat of melting water as a function of melting temperature (or respective pressure): circle, 2% gelatine gels; diamond, 10% gelatine gels.


decreases. Thus, it can be concluded that the amount of the bound water in gelatine gels increases with pressure.

Pressure Shift Freezing

Pressure shift freezing (PSF) is increasingly receiving attention in recent years because of its potential benefi ts for improving the quality of frozen food. Generally, the PSF process consists of three successive steps: (1) cooling the product under pressure to a low temperature (e.g., 253 K at 200 MPa) without involving phase change; (2) a quick depres-surization (adiabatic expansion) to create supercooling for instanta-neous, uniform, and partial initiation of the freezing process (resulting largely in ice nucleation); and (3) completion of the freezing process (ice crystal growth) under atmospheric pressure. It has been demon-strated that the PSF process produces fi ne and uniform ice crystals throughout the food samples (Chevalier, Le Bail, and Ghoul 2002 ), thus reducing ice crystal - related textural damage to frozen products (Chevalier et al. 2001 ). In the PSF process, after the pressure release, a portion of the liquid water is frozen, and the resulting crystals are usually very small in size (like ice nuclei in conventional freezing) and will then grow when the freezing is completed under atmospheric pres-sure. Evaluation of the amount of ice nuclei formed instantaneously by depressurization is important for a better understanding of PSF process. The high - pressure calorimetry can be very helpful in this respect (Zhu, Ramaswamy, and Le Bail 2005 ). Figures 13.16 and 13.17

Coolin

g

273

G

F

A B

E

C

D Depressurization

Ice-I

Water

Pressurization

252

0.1 Pnuc 210 Pressure (MPa)

Tem

pera

ture

(K

)

Coolin

g

Superc

oolin

g

Nucle

ation

Figure 13.16. Basic procedure of pressure - shift freezing based on phase transition between water and ice I under pressure; P nuc is the pressure under which ice nucleation starts.


present procedures of a PSF experiment performed in a high - pressure calorimeter using pork muscle under pressure of 199 MPa at 253.1 K. After placing the sample into the experimental vessel, the calorimeter was rapidly pressurized to target level (AB in Figure 13.16 ). Then the sample was cooled to the preset temperature under constant pressure (BC in Figure 13.16 ). When the baseline of the calorimetric signal became stable (AB in Figure 13.17 ), pressure was released to initiate the nucleation process (CDEF in Figure 13.15 and BC in Figure 13.17 ). Because the depressurization was carried out rapidly (within a matter of 1 or 2 s), water was instantaneously supercooled in the liquid state (a metastable one), even after the complete release of pressure (i.e., P nuc = 0.1 MPa and EF in Figure 13.16 ), and then ice nucleation occurred. After ice nucleation, sample temperature increased to the freezing point (EF in Figure 13.16 ) due to the latent heat of crystalliza-tion. Finally, the sample was allowed to complete freezing under atmo-spheric pressure (FG in Figure 13.16 and CD in Figure 13.16 ). Figure 13.18 shows the ratio of ice crystal to the whole mass of the sample formed during depressurization of the pork, determined from the high - pressure calorimetric results described above.

Gelatinization of Starch

Starch is one of the most important natural macromolecules. Its impor-tance stems from the fact that the starch granule is an almost universal

400Temperature

C

Heat flux

PressureA B

Heat flux (

mW

/g)

or

Pre

ssure

(M

Pa)

300

200

100

0

–100

253.1

252.1

0

251.10 1200 2400

Time (s)

3600 4800

Tem

pera

ture

(K

)

Figure 13.17. Typical profi les of pressure, heat fl ux, and temperature during pres-sure - shift freezing of pork muscle under pressure of 199 MPa at 253.1 K.


form for packaging and storing carbohydrate in green plants. It is also one of the main components of food materials, especially those submit-ted to elevated pressure extruder processing.

The process of preparing a homogeneous sol phase from a mixture of native starch and water is called gelatinization. Starch gelatinization is a combined process consisting of hydration of amorphous regions and subsequent melting of crystalline arrays. It was demonstrated recently (Hayert et al. 2003 ) that all the transformations occurring during starch gelatinization can be observed with a high - sensitivity DSC done at a low rate of temperature scanning under atmospheric pressure. Typical results are shown in Figure 13.19 for pastes or emul-sions of wheat starch with various total water contents. The main endothermic transition occurring from 319 K to 333 K independently of the water content is likely associated with melting of the crystalline part of the starch granules followed by a helix - coil transformation in amylopectin, the main component of starch. This endothermic transi-tion is followed by a water - dependent, slow, exothermic transforma-tion, which is probably related to reassociation of the unwound helices of amylopectin with parts of amylopectin molecules other than their original helix - duplex partner, forming physical junctions and creating more general hydrogen - bonded associations. The high - temperature endothermic transition occurring at water contents around 50 wt % and

Tem

pera

ture

(K

)

Ratio o

f ic

e to s

am

ple

mass (

%)

293

273

253

40

30

50

20

10

0

0.1 50 100 150

Pressure (MPa)

200

Ice ratio and regression curve

Phase-change curve

250

Figure 13.18. Ratios of ice crystal to sample mass instantaneously formed after depressurization during pressure - shift freezing of pork muscle (74.2% moisture content) at various initial pressures, with temperatures slightly higher than the cor-responding phase - change points of water.


higher is associated with destruction of amylose - lipid complexes or with a nematic - isotropic transition, which ends the formation of the isotropic colloidal SOL phase. The pressure infl uence on those transi-tions has been investigated under the process conditions of pressure and temperature, using a scanning transtitiometer described above. A mixture of native wheat starch with 50 wt % of added water (56.0 wt % total water content) has been selected for such transitiometric high - pressure studies (Randzio and Orlowska 2005 ).

Figure 13.20 presents results obtained in isobaric experiments by scanning temperature at a low rate of 2.5 mK s − 1 (0.15 K min − 1 ) under pressures of 10, 60, and 100 MPa. Results at each pressure present two output signals recorded simultaneously as a function of temperature, the heat fl ux and dV/dT (thermal expansion), both quantities expressed per gram of dry starch. The most important observation is that all the transitions recorded previously under atmospheric pressure at a temperature - scanning rate of 16.7 mK s − 1 (1 K min − 1 ) with a high - sensitivity DSC (see the respective trace at 56.0 wt % of total water

–26

–28

–30

–32

–34

–36

–38

–40300 310

64.8

60.3

56.0 MA

N

46.8Endo

320

Heat flux d

iffe

rence (

mW

/g)

330 340

Temperature (K)

350 360 370 380

Figure 13.19. DSC traces obtained at atmospheric pressure at a rate of 16.67 mK s − 1 for aqueous native wheat starch emulsions at selected concentrations of water (total water contents in wt %). M, main endothermic transition, occurring from 319 K to 333 K independently of the water content; A, water - dependent, slow, exothermic transformation; N, high - temperature endothermic transition occurring at water con-tents around 50 wt % and higher.

Figure 13.20. Transitiometric traces (heat fl ux and d V/ d T per gram of dry starch) obtained simultaneously and under the process conditions of pressure and temperature by scanning temperature at a rate of 2.5 mK s − 1 at various pressures for a starch - water emulsion (56.0 wt % total water content). A, exothermic transformation; N, endother-mic transition.

333


content in Figure 13.19 ) also are observed in the transitiometric traces in Figure 13.20 performed under elevated pressures at a much lower temperature - scanning rate of 2.5 mK s − 1 (0.15 K min − 1 ). The transitio-metric method also measures simultaneously the volume changes at those transitions. In Figure 13.19 , the right ordinate presents the dV/dT of the sample; the d V/ d T from the hydraulic liquid (see Figure 13.10 ) have been subtracted from the experimental data. The d V/ d T of the sample at the main endothermic transition (M) decreases over the pressure range under investigation (10 – 100 MPa). Also note that the changes of d V/ d T at the particular transitions are rather small, while the general tendency is for d V/ d T to rise considerably with temperature over the whole temperature range.

The last phenomenon is associated with swelling of starch granules during gelatinization, even under elevated pressures. This allows a more detailed analysis of the main transition (M). For several degrees prior to and after the transition, d V/ d T increases linearly with tempera-ture with the same or very similar slope. Assuming this, d V/ d T during the transition can be divided into two contributions, one due to the assumed linear swelling and one due to the phase transition itself. Figure 13.21 presents an example of such a division of the results obtained at 10 MPa. Once the division is made, the two contributions can be integrated separately to give volume changes occurring in the transition, a positive change for the swelling and a negative change for the transition itself.

Figure 13.21. Division of d V/ d T for the main endothermic transition into a positive d V/ d T due to swelling and a negative d V/ d T due to the transition itself.


Integrated volumetric data, together with thermal data, all obtained for each pressure from at least four independent experiments and per-formed each time on a freshly prepared sample, are given in Table 13.1 . The errors are the standard deviations from the mean values of all experiments performed at each pressure. Table 13.1 also contains data at 0.1 MPa obtained previously (Randzio, Flis-Kabulska, and Grolier 2002 ) with a DSC. From linear approximations of pressure dependence of the parameters presented in Table 13.1 , the following slopes could be obtained: d H trans /d p = − (9.85 ± 2.25) mJ MPa − 1 g − 1 , d V trans /d p = 2.27 ± 0.37 10 − 3 mm 3 g − 1 MPa − 1 , d V swelling /d p = − (9.28 ± 2.05) 10 − 3 mm 3 g − 1 MPa − 1 and dT trans /dp = − (24.6 ± 6.9) mK MPa − 1 . Assuming the main transition (M) is an equilibrium fi rst - order transition, the last slope also can be obtained from the Clapeyron equation and data from Table 13.1 , from 10 MPa to 100 MPa (d T trans /d p ) Clapeyron = − (78.3 ± 2.5) mK MPa − 1 . Although the slopes are both negative, the agreement is poor, implying that the mechanism of the transition is more compli-cated than the fi rst - order transition assumed by the Clapeyron equation. Also, the pressure dependence may not be linear, especially at low pressure. Future studies will focus on that problem.

Despite a large number of pressure studies on starch gelatinization, only the results of Rubens and Heremans (Rubens and Heremans 2000 ) were obtained from under the process conditions of pressure and tem-perature studies and are in agreement with the present results. In Figure

Table 13.1. Thermodynamic data for the main transition (M) in an aqueous emulsion of wheat starch (56 wt % total water) expressed per gram of completely dry starch.

Quantity

Pressure (MPa)

0.1 10 60 100

Δ trans H (Jg − 1 )

3.52 ± 0.07 3.12 ± 0.12 2.65 ± 0.07 2.45 ± 0. 7

Δ trans V (mm 3 g − 1 )

— − 0.788 ± 0.038 − 0.647 ± 0.040 − 0.586 ± 0.035

Δ swelling V (mm 3 g − 1 )

— 1.53 ± 0.10 1.22 ± 0.05 0.683 ± 0.036

T trans,onset (K) 320.5 ± 0.5 319.5 ± 0.5 318.1 ± 1.0 317.9 ± 0.6

Source: Data at 0.1 MPa are from Randzio et al. (Randzio, Flis-Kabulska, and Grolier 2002 ).


4 of their study, performed using infrared spectroscopy and a diamond anvil cell, Rubens and Heremans (Rubens and Heremans 2000 ) show that the temperature of the transition is lowered by pressure increase. In opposition to the pressure effects on the main endothermic transition (M), the pressure infl uence on both the exothermic transformation (A) and the high - temperature endothermic transition (N) is positive. At 10 MPa, the exothermic transformation starts at 348.6 ± 0.6 K and is shifted by pressure to higher values at a mean rate of 38.9 ± 9.9 mK MPa − 1 . Also at 10 MPa, the high - temperature endothermic transition starts at 382.7 ± 0.2 K and is shifted by pressure to higher values at a mean rate of 96.1 ± 3.4 mK MPa − 1 . These observations are in agreement with a general thermodynamic approach to these transitions.

In Figure 13.20 , in transition (A), the exothermic effect is always associated with a decrease of thermal expansion, which is most prob-ably caused by a negative volume change at that transition, similar to the observation made on the main transition (M). In transition (N), the endothermic effect is associated with a small increase of thermal expansion, which is most probably caused by a positive volume change at that transformation. Thus, the Clapeyron equation in both cases also would give positive d T /d P slopes.

The uncertainty limits given for the above results contain both a purely instrumental contribution, 1 – 5%, and a contribution from the preparation of the emulsion, which can be several percent.

Phase Stability of Systems Containing Lipids

Lipids are components of various food products, especially oils. The content and the nature of lipids infl uence the phase stability of such products. Figure 13.22 presents a pressure - temperature phase diagram (a) and pressure dependence of latent heat of fusion (b) for cocoa butter, palm oil, copra oil, and for comparison, water. All the data were obtained from high - pressure calorimetric measurements (Hayert et al. 2003 ). It can be seen that the cocoa butter and copra oil, which do have almost no high unsaturated fatty acids, solidify at rather low pressures below 60 MPa. In contrast, the palm oil, which contains more high unsaturated fatty acids, solidifi es under much higher pressure of 122 MPa. All the transitions in the investigated systems containing lipids have positive slopes and their latent heats do not depend on pressure, which can suggest that the volume variations of those transi-


Figure 13.22. (a) PT phase diagram of lipids (cocoa butter, palm oil, copra oil) and water determined from high - pressure calorimetric measurements. (b) Pressure depen-dence of latent heat of fusion of lipids (cocoa butter, palm oil, copra oil) and water determined from high - pressure calorimetric measurements.

tions are positive. These features are in opposition to the respective properties of water, which should be taken into consideration in high - pressure processing of food products containing those components.

Conclusions

Presented in this chapter are a review and a description of high - pressure calorimetric and transitiometric techniques, which allow


investigation under typical processing conditions of high hydrostatic pressure and temperature of various transitions and processes occur-ring in model and real food systems under well - determined pressure and temperature conditions. The thermodynamic and thermomechanic parameters of the transitions under investigation determined with the described techniques permit understanding of the effects of high - pres-sure processing on the physical properties of food materials and thus should allow prediction of the formulation of raw materials and pro-cessing conditions so as to achieve desired end - product properties. A detailed description of selected applications of the presented tech-niques in the analysis of high - pressure processing of selected systems important for food science and technology should also stimulate further development of such applications in other food systems.

References

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Bridgman , P.W. 1914 a. The coagulation of albumin by pressure . J Biol Chem , 19 : 511 .

Bridgman , P.W. 1914 b. Change of phase under pressure. I. Phase diagrams of eleven substances . Phys Rev , 3 : 153 .

Cheftel , J.C. , Levy , J. , and Dumay , E. 2000 . Pressure - assisted freezing and thawing: Principles and potential applications . Food Rev Int , 16 : 453 .

Cheftel , J.C. , Thiebaud , M. , and Dumay , E. 2002 . Pressure assisted freezing and thawing: A review of recent studies . High Press Res , 22 : 601 .

Chevalier , D. , Le Bail , A. , and Ghoul , M. 2002 . Freezing and ice crystals formed in cylindrical model food. Part II. Comparison between freezing at atmospheric pres-sure and pressure shift freezing . J Food Eng , 46 : 287 .

Chevalier , D. , Sequeira - Munoz , A. , Le Bail , A. , Simpson , B.K. , and Ghoul , M. 2001 . Effect of freezing conditions and storage of ice crystals and drip volume in turbot ( Scophthalmus maximus ), evaluation of pressure shift freezing vs. air - blast freez-ing . Innov Food Sci Emerg Technol , 1 : 193 .

Chevalier - Lucia , D. , Le Bail , A. , Ghoul , M. , and Chourot , J.M. 2003 . High pressure calorimetry at sub - zero temperature: Evaluation of the latent heat and frozen water ratio of gelatine gels . Innov Food Sci Emerg Technol , 4 : 361 .

Chourot , J.M. , LeBail , A. , and Chevalier , D. 2000 . Phase diagram of aqueous solution at high pressure and low temperature . High Press Res , 19 : 191 .

Douzals , J.P. , Perrier - Cornet , J.M. , Coquille , J.C. , and Gervais , P. 2001 . Pressure - temperature phase transition diagram for wheat starch . J Agric Food Chem , 49 : 873 .


Fennema , O.R. 1973 . Nature of freezing process . In: Low Temperature Preservation of Foods and Living Matter , O.R. Fennema , W.D. Powrie , and E.H. Marth , editors, pp. 151 – 222 . Marcel Dekker : New York .

Hayashi , R. 2002 . High pressure in bioscience and biotechnology: Pure science encompassed in pursuit of value . Biochim Biophys Acta , 1595 : 397 .

Hayert , M. , Le Bail , A. , Rigenbach , M.H. , and Gruss , E. 2003 . High pressure calo-rimetry as a tool to monitor phase transitions in foods: Application to water and selected lipids . In: Advances in High Pressure Bioscience and Biotechnology II , R. Winter , editor. Springer Verlag : London .

Kaletun ç , G. and Breslauer , K.J. 1996 . Construction of wheat - fl ower state diagram . J Therm Anal , 47 : 1267 .

Kalichevsky , M.T. , Knorr , D. , and Lillford , P.J. 1995 . Potential applications of high - pressure effects on ice - water transitions . Trends Food Sci Tech , 6 : 253 .

Knorr , D. 1999 . Process assessment of high - pressure processing of foods: An over-view . In: Processing Foods: Quality Optimization and Process Assessment , F.A.R. Oliveira and J.C. Oliveira , editors, p. 249 . CRC Press : Boca Ration, FL .

Le Bail , A. , Boillereaux , L. , Davenel , A. , Hayert , M. , Lucs , T. , and Monteau , J.Y. 2003 . Phase transition in foods: Effect of pressure and methods to asses or control phase transition . Innov Food Sci Emerg Technol , 4 : 15 .

Le Bail , A. , Chevalier , D. , Chourot , J.M. , and Monteau , J.Y. 2001 . High pressure calorimetry. Comparison of two systems (differential vs . single cell). Application to the phase change of water under pressure . J Therm Anal Calorim , 66 : 243 .

Pham , Q.T. 1987 . Calculation of bound water in frozen food . J Food Sci , 52 : 210 . Randzio , S.L. 1985 . Scanning calorimeters controlled by an independent thermody-

namic variable: Defi nitions and some metrological problems . Thermochim Acta , 89 : 215 .

Randzio , S.L. 1996 . Scanning transitiometry . Chem Soc Rev , 25 : 383 . Retrieved from: http://www.transitiometry.com

Randzio , S.L. 1997 . State variables in calorimetric investigations: Experimental results and their theoretical impact . Thermochim Acta , 300 : 29 .

Randzio , S.L. 1998 . Recent developments in calorimetry . Ann Rep Prog Chem, Sect C , 94 : 433 .

Randzio , S.L. 2002 . Recent developments in calorimetry . Ann Rep Prog Chem, Sect C , 98 : 157 .

Randzio , S.L. , Flis - Kabulska , I. , and Grolier , J.P.E. 2002 . Re - examination of phase transitions in the starch - water system . Macromolecules , 35 : 8852 .

Randzio , S.L. , Grolier , J.P.E. , and Quint , J.R. 1994 . An isothermal scanning calorim-eter controlled by linear pressure variations from 0.1 to 400 MPa. Calibration and comparison with the piezothermal technique . Rev Sci Instrum , 65 : 960 .

Randzio , S.L. and Orlowska , M. 2005 . Simultaneous and under the process conditions of pressure and temperature analysis of thermal and volumetric properties of starch gelatinization over wide pressure and temperature ranges . Biomacromolecules , 6 : 3045 .

Rodier - Renaud , L. , Randzio , S.L. , Grolier , J.P.E. , Quint , J.R. , and Jarrin , J. 1996 . Isobaric thermal expansivities of polyethylenes with various crystallinities over


the pressure range from 0.1 MPa to 300 MPa and over the temperature range from 303 K to 393 K . J Polym Sci Pol Phys Ed , 34 : 1229 .

Rubens , P. and Heremans , K. 2000 . Pressure - temperature gelatinization phase diagram of starch: An under the process conditions of pressure and temperature Fourier transform infrared study . Biopolymers , 54 : 524 .

Smeller , L. 2002 . Pressure - temperature phase diagrams of biomolecules . Biochim Biophys Acta , 1595 : 11 .

Stute , R. , Heilbronn , R.W. , Boguslawski , S. , Eshtagi , M.N. , and Knorr , D. 1996 . Effects of high pressure treatment on starches . Starch/St ä rke , 48 : 399 .

Zhu , S. , Bulut , S. , Le Bail , A. , and Ramaswamy , H.S. 2004 . High - pressure differential scanning calorimetry (DSC): Equipment and technique validation using water - ice phase - transition data . J Food Proc Eng , 27 : 359 .

Zhu , S. , Ramaswamy , H.S. , Le Bail , A. 2004 . High - pressure differential scanning calorimetry: Evaluation of phase transitions in pork muscle at high pressures . J Food Proc Eng , 27 : 377 .

Zhu , S. , Ramaswamy , H.S. , and Le Bail , A. 2005 . High - pressure calorimetric evalu-ation of ice crystal ratio formed by rapid depressurization during pressure - shift freezing of water and pork muscle . Food Res Int , 38 : 193 .

Chapter 14

Calorimetric Analysis of Starch Gelatinization by High - Pressure Processing

Kelley Lowe and G ö n ü l Kaletun ç

341

Introduction 341 Gelatinization of Starch by Heat 342 Gelatinization of Starch by High Hydrostatic Pressure 344

High - Pressure Processing of Wheat Starch Suspensions 344 Storage of Gelatinized Starch 347 Conclusions 348 References 349

Introduction

Starches are used in many food products to increase viscosity or to form gels. Because starch is insoluble in water, a mixture of starch with water forms a suspension. Starch granules in suspension swell with heat, and the viscosity of the suspension increases, depending on starch concentration. Thermal processing changes the physicochemical properties of starch, such as increased water solubility and devel-opment of viscoelastic behavior (Fennema 1996 ; Jobling, 2004 ). Because starch affects the texture of food products, characterization of aqueous starch suspension behavior and its interaction with other food additives under conditions relevant to food processing and storage is important for assessment of food stability. This chapter provides a review of starch characterization studies by calorimetry relevant


to thermal processing, high hydrostatic pressure (HHP) processing, and retrogradation of processed starch during storage.

Gelatinization of Starch by Heat

Aqueous starch suspension undergoes a phase transition between 60 ° C and 70 ° C. During the phase transition, starch granules swell and optical properties of starch granules, such as light polarization or iodine color-ation, change. Starch gelatinization is a water - assisted melting process that exhibits an endothermic transition and is monitored by differential scanning calorimetry (DSC). Figure 14.1 shows the gelatinization endo-therms of 15% (w/w) for wheat and corn starches heated in a DSC. The peaks observed are characterized by two parameters, namely, the peak temperature, thermal stability of the starch phase; and the peak area under the curve, enthalpy of gelatinization ( Δ H ). It is apparent that the gelatinization temperature of corn starch is higher than that of wheat starch in terms of onset (66 ° C vs. 58 ° C) and peak temperatures (70 ° C vs. 64 ° C) of the gelatinization endotherm. Corn starch also requires a larger heat energy for gelatinization per gram of dry starch (11.2 Jgds − 1 vs. 16.1 Jgds − 1 ). Similar results are reported in the literature for starches of different origins (Roos 1995 ). Because thermally induced gelatiniza-tion requires higher temperatures and exhibits larger enthalpy change for corn starch than wheat starch, corn starch is considered to have a more thermally stable crystalline structure.

Starch exists in foods together with other ingredients. Gelatinization characteristics of starch are also affected by the presence of other food ingredients, such as sugars and proteins (Figure 14.2 ). Curve C in Figure 14.2 is a thermogram of a mixture of starch, sugar, and milk protein, which is a representative composition of milk pudding. Figure 14.2 shows that when the total dry matter is kept constant, replacing some of the starch with sugar only (curve B) and sugar and proteins (curve C) shifts the thermal stability of the corresponding mixture to higher temperatures up to approximately 5 ° C in comparison with the thermal stability of the starch - water mixture (curve A). Similarly, the heat energy required for gelatinization changes with the composition of the mixture. Therefore, the parameters obtained by calorimetry are directly applicable to processing protocols and should be taken into account when process conditions are selected.

Heat F

low

(W

/g)

–0.32

65.72˚C2.416J/g

58.30˚C1.673J/g

63.63˚C

70.33˚C

–0.34

–0.36

–0.38

–0.40

–0.4245 50 55 60 65 70

Temperature (˚C)

Exo Up

75 80 85 90

Universal V2.6D TA Instruments

Figure 14.1. DSC thermograms of 15% (w/w) native corn and wheat starch suspen-sions. Corn starch (solid line) and wheat starch (dashed line).

30 40

A

B

C

50 60 70

Temperature (˚C)

80 90 100

Universal V2.6D TA Instruments

Figure 14.2. DSC thermograms of wheat starch (A), wheat starch - sugar (B), and wheat starch - sugar - milk protein (C). Total solids: 30% for all cases.

343


Gelatinization of Starch by High Hydrostatic Pressure

HHP has been shown to affect high - molecular - weight polymers causing denaturation of proteins (Messens et al. 1997 ; Famelart et al. 1998 ) and gelatinization of starch (Douzals et al. 1996 , 1998 , 2001 ; Zuo et al. 1999 ). Ezaki and Hayashi ( 1992 ), based on a study including 20 starches, reported that A - type starches (cereals) are more suscep-tible to high pressure than B - type starches (tubers), and C - type starches (pea, tapioca) show intermediate behavior. A similar study conducted by Stute et al. (1996) on two starches of various crystalline structures stated that A - and C - type starches are susceptible to gelatinization at intermediate pressure levels, and B - type starches are the most pressure resistant. Among different starches, potato starch appears to be the most resistant starch to high - pressure gelatinization. Studies in the literature show that starch can be gelatinized by pressure partially depending on the level of pressure applied (Ezaki and Hayashi 1992 ; Stute et al. 1996 ; Douzals et al. 1998 ).

High - Pressure Processing of Wheat Starch Suspensions

The impact of HHP on gelatinization of starch is investigated by applying a pressure treatment between 0.1 and 700 MPa to a 30% wheat starch suspension. The high - pressure - processed starch is then characterized by performing DSC studies. A preliminary experi-ment at various starch concentrations showed that it requires at least 12% wheat starch concentration to form a gel by using HHP processing. Although the DSC results exhibited a complete gelati-nization, a characteristic gel texture cannot be obtained below 12% wheat starch concentration. A similar observation was reported by Stolt et al. (2001) for barley starch: while the increase of viscosity was slight for 10% suspension after pressure treatment at 550 MPa, a strong paste with creamy texture was obtained with 25% suspension. Stute et al. (1996) also stated that HHP processed starches exhibited low viscosity values at the concentrations at which gel formation typically occurs by heat gelatinization, but they formed smooth pastes or rigid gels within the concentration range from 15% to 30% dry matter.

Calorimetric Analysis of Starch Gelatinization 345

Sample p reparation A Hydrostatic High Pressure Unit (Quintas QFP - 6; ABB Autoclave Systems, Columbus, OH) with a maximum pressure of 900 MPa was used to gelatinize the starch suspensions at ambient temperature. A water - ethylene glycol (1 : 1 vol/vol) mixture was used as the pressure - transmitting fl uid. The rate of pressurization was 400 MPa/min, with a pressure release time of less than 20 s. During pressurization, the pres-sure, temperature, and time were kept constant using an automatic device and recorded throughout the cycle using a data logger. Fifty milliliters of 30% starch suspension was placed in a sterile, polyethyl-ene bag and sealed under vacuum. The sample bags were placed inside the Hydrostatic High Pressure Unit. HHP processing was performed at 25 ° C for 15 min at a range of pressures from 100 MPa to 700 MPa.

The pressure - treated samples were analyzed using a DSC (model 2090; TA Instruments, New Castle, DE) to determine the degree of gelatinization. Samples (50 – 55 mg) of HHP - treated starch were placed in a high - volume stainless steel crucible, and the thermograms were recorded from 1 ° C to 100 ° C at a heating rate of 5 ° C/min. Each ther-mogram was analyzed to calculate the onset and the peak temperatures and the enthalpy of the endothermic transition corresponding to the melting of ungelatinized starch.

Results The degree of gelatinization of a 30% starch solution is directly related to the level of pressure applied at 25 ° C. Figure 14.3 shows that the peak area corresponding to melting of ungelatinized starch decreases progressively as the level of applied pressure is increased. In addition, as the pressure level increases, the thermal stability of the ungelati-nized starch phase decreases, indicating changes in the crystal structure of starch, although some light microscopy studies show intact starch granules after pressure treatment (Douzals et al. 1998 ; Stolt et al. 2001 ). However, some microscopic observations showed some swell-ing of starch granules (Douzals et al. 1996 ), which could lead to the observation of reduced thermal stability.

The data in Figure 14.3 were analyzed further to calculate the per-centage of gelatinized starch as a function of applied pressure (Figure 14.4 ). Percent starch gelatinization data from Douzals et al. (1996) and Stute et al. (1996) for wheat starch also are included in Figure 14.4 . Although the initial starch concentrations are different, 16% (Douzals

Heat F

low

(W

/g)

–0.30

–0.32

–0.34

–0.36

100

–0.38

–0.4050 55 60 65 70 75 80

Temperature (˚C)

Exo Up Universal V2.6D TA Instruments

200

300

400

500

600

Figure 14.3. Gelatinization endotherm of 30% wheat starch suspension after HHP processing at various pressure levels.

Pressure (MPa)

Perc

ent gela

tiniz

ation

100

80

60

40

20

0 100 200 300 400 500 6000

Figure 14.4. Percent gelatinization of wheat starch as a function of HHP. Open square, 30% starch (this study); fi lled circle, 16% wheat starch (data taken from Douzals et al. 1996 ); fi lled diamond, 25% wheat starch (data taken from Stute et al. 1996 ).

346


et al. 1996 ), 25% (Stute et al. 1996 ), and 30% (w/w) dry solids (this work), the trend seems to be similar. A fairly sharp increase in the extent of gelatinization starts at 300 MPa for all starch data, and wheat starch suspensions are completely gelatinized after a treatment at 500 – 600 MPa. The results suggest that starch can be partially or completely gelatinized by increasing pressure at constant temperature.

Douzals et al. (2001) further characterized the behavior of the wheat starch - water suspensions at 5% dry starch concentration over a pres-sure range of 0.1 and 600 MPa and over the temperature range of − 20 ° and 96 ° C. The microscopic measurements of the loss of birefringence of the granules calibrated by DSC was used to determine the extent of gelatinization. The data were used to develop the pressure - temperature (P - T) gelatinization diagram. The P - T gelatinization diagram is similar to the P - T diagram of denaturation of proteins. The P - T diagram indi-cates that starch can be gelatinized under various pressure - temperature combinations; however, the gelatinized starch can have different prop-erties based on the gelatinization conditions.

For corn starch, Zuo et al. (1999) report the presence of native starch after a treatment at 700 MPa for 2 min, but complete gelatinization after 5 min, which indicates that starch gelatinization by pressure is a kineti-cally controlled process. This issue becomes signifi cant for investiga-tion of starch kinetics at HHPs. The starch gelatinization kinetics should be decoupled from the rate of increase of pressure with time. In fact, Stolt et al. (2001) state that treatment of barley starch suspension at 550 MPa with a zero - minute holding time exhibited almost complete gelatinization, indicating pressurization at a rate of 20 MPa · min − 1 was suffi ciently slow for complete gelatinization.

Storage of Gelatinized Starch

Gelatinized starch recrystallizes during storage, affecting the texture and shelf life of food products. This phenomenon is known as retrogra-dation. Retrogradation contributes to the quality defects in foods, such as loss of viscosity, syneresis, and precipitation. Jouppila et al. (1998) reported that water content, storage temperature, and the temperature difference between storage temperature and glass transition tempera-ture were important factors in retrogradation of thermally treated corn starch. Furthermore, the formation of bonds between the macromole-


cules during retrogradation depends on the gelatinization conditions, including concentration and degree of gelatinization (Stute et al. 1996 ).

Douzals et al. (1998) reported that crystallization of the HHP - gelatinized wheat starch (30% dry matter) during storage approached an asymptotic value after 6 days, and the extent of retrogradation was higher for starch gelatinized by heat than starch gelatinized by pressure at 600 MPa. However, Stolt et al. (2001) reported that the retrograda-tion of heat - treated (90 ° C, 30 min) and pressure - treated (550 MPa, 30 ° C, 10 min) 25% (w/w) barley starch stored at 4 ° C was similar and did not approach an asymptotic value after 7 days of storage. The results may suggest that retrogradation depends on the botanical source of the starch as well as gelatinization conditions, storage temperature, and starch concentration.

Conclusions

An understanding of the effect of high pressure on the properties of starch - based food systems under conditions relevant to food storage are necessary to predict storage stability of such systems so that HHP - processing protocols can be optimized for successful development of HHP - processed commercial food products.

Studies on the retrogradation properties of starch in the presence of common food ingredients are also essential for optimization of pro-cessing conditions so as to improve the physical stability and textural characteristics of HHP - processed food products. Because DSC instru-ments operating under conditions relevant to HHP - processing condi-tions are not commercially available, starch samples have to be treated before performing DSC on the samples. The thermodynamic basis of starch gelatinization involves initial and fi nal states that can be experi-mentally defi ned and energetic and/or structural differences that can be measured using calorimetry. The comparison of various fi nal states as a function of exposure to various levels of pressure, starting from the same initial state, makes it possible to predict the effectiveness of HHP to produce gelatinized starch. However, development of DSC equipment working at high pressures will allow one to perform pres-sure scans to determine the pressure dependence of the gelatinization event, to investigate the kinetics of starch gelatinization at constant pressure, and to separate the irreversible and reversible events.


References

Douzals J.P. , Marechal P.A. , Coquille J.C. , and Gervais P. 1996 . Microscopic study of starch gelatinization under high hydrostatic pressure . J Agric Food Chem , 44 ( 5 ): 1405 – 1409 .

Douzals J.P. , Perrier - Cornet J.M. , Gervais P. , and Coqulle J.C. 1998 . High - pressure gelatinization of wheat starch and properties of pressure - induced gels . J Agric Food Chem , 46 : 4824 – 4829 .

Douzals J.P. , Perrier - Cornet J.M. , Coqulle J.C. , and Gervais P. 2001 . Pressure - temperature phase transition diagram for wheat starch . J Agric Food Chem , 49 : 873 – 876 .

Ezaki S. and Hayashi R. 1992 . High - pressure effects on starch: Structural changes and retrogradation . In: High Pressure and Biotechnology , Vol. 224 , Balny C. , Hayashi R. , Heremans M.P. , editors, pp. 163 – 165 . Colloque INSERM/John Libbey Eurotext : Montrouge, France .

Famelart M.H. , Chapron L. , Piot M. , Brule G. , and Durier C. 1998 . High pressure - induced gel formation of milk and whey concentrates . J Food Eng , 36 : 149 – 164 .

Fennema O.R. 1996 . Food Chemistry , 3rd edition , pp. 201 – 204 . Marcel Dekker : New York .

Jobling S. 2004 . Improving starch for food and industrial application . Cur Opin Plant Biol , 7 : 210 – 218 .

Jouppila K. , Kansikas J. , and Roos Y.H. 1998 . Factors affecting crystallization and crystallization kinetics in amorphous corn starch . Carbohyd Polym , 36 : 143 – 149 .

Messens W. , Van Camp J. , and Huyghebaret A. 1997 . The use of high pressure to modify the functionality of food proteins . Trends Food Sci Technol , 8 : 107 – 112 .

Roos Y.H. 1995 . Phase Transitions in Food . Academic Press : San Diego, CA . Stolt M. , Oinonen S. , and Autio K. 2001 . Effect of high pressure on the physical

properties of barley starch . Innov Food Sci Emerg Technol , 1 : 167 – 175 . Stute R. , Heilbronn R.W. , Klingler R.W. , Boguslawski S. , Eshtiaghi M.N. , and Knorr

D. 1996 . Effects of high pressures treatment on starches . Starch/Staeke , 48 : 399 – 408 .

Zuo C. , Ma , C. , and Zhang S. 1999 . Effects of high hydrostatic on gelatinization of corn starch . In: Proceedings of the International Conference on Agricultural Engineering (ICAE) , Vol. 4, pp. 91 – 93 Beijing, China .

Chapter 15

Use of Calorimetry to Evaluate Safety of Processing

Hans Fierz

351

Scope 352 Concepts 352

Severity: Adiabatic Temperature Rise 353 Probability: Time to Maximum Rate 353 Critical Conditions 354 Autocatalysis 356

Differential Scanning Calorimetry 356 Screening 356 Comparison of Open and Closed Measurement Methods 357 Estimation of q ′ ( T ) 357 Isoconversional Methods 360 High - Sensitivity Calorimetry 361

Adiabatic Measuring Methods 361 Dewar Vessels 361 Accelerating Rate Calorimeter 362

Reactions with Oxygen 363 Screening Test 363 Determination of Self - Ignition Temperature 364

Applications 364 Formation of Hot Spots in Dryers 364 Storage and Hot Discharge 364 Prevention of Molasses Incidents 365 Transport Safety 365



Scope

Food, as does every other chemical, shows chemical reactivity, and if handled in bulk can be dangerous. Numerous incidents involving wheat, milk powder, coffee, or molasses are known, due to, for example, self - heating, self - ignition of hot spots, or dust explosions.

This chapter focuses on the methodology for characterizing the thermal consequences of exothermic decompositions in bulk and the correspondent safety risks. Of course, there are desired synthetic exo-thermic reactions in bulk, with food presenting a thermal risk, as for example the hydrogenation of fats. These cases are rather rare and may not justify a treatment from a specialized point of view such as food chemistry.

However, the methodology of how to treat and quantify the risk of both desired reactions and decompositions is not specifi c to food chem-istry, but was developed for process chemistry in general. A thorough discussion of this topic can be found in books about process safety (Stoessel 2008 ).

One of the major risks in food production is dust explosions in mills or dryers, for example, in sugar refi ning. Because this chapter deals with applications of calorimetry, we consider dust explosions only insofar as hot spots serve as ignition sources. Further literature about this topic can be found in Bartknecht (1981) .

Concepts

The risk of an exothermic decomposition can be defi ned as the product of its severity and its probability, both of which can be discussed within the framework of the so - called adiabatic scenario.

Adiabatic means that there is no heat exchange at all between the system and the surroundings. A situation with no or negligible heat exchange could occur in various cases, including failure of heat trans-fer (breakdown of stirrer or cooling system) during storage of a liquid or storage of reactive solids in bulk.

The latter situation can occur either intentionally, for example, when a solid is stored in drums or containers at elevated temperature, or unintentionally in a dryer or a mill when after a technical incident the product is no longer agitated and the product cannot be discharged.

Use of Calorimetry to Evaluate Safety 353

Severity: Adiabatic Temperature Rise

In an ideal adiabatic environment any heat generated by a decomposi-tion will be accumulated and converted to a temperature rise, Δ T ad ,

ΔT Q

cr

pad = ′

(15.1)

where Q ′ r is the specifi c heat of reaction and c p the specifi c heat capacity.

The adiabatic temperature rise is a measure of the severity of an incident. An adiabatic temperature rise of 50 K can hardly be consid-ered critical, but one of 400 K could lead to formation of gases, to an explosive rupture of the vessel, and fi nally to a fi re due to self - ignition of the dispersed material.

Probability: Time to Maximum Rate

The rate of an exothermic reaction is proportional to its heat release rate and if no heat can be removed, to its temperature increase rate:

ddTt

qc

r Qcp

r

p= ′ = ′ ⋅ ′

(15.2)

where q ′ is the specifi c heat production in watts per kilogram and r ′ is the specifi c reaction rate in kilograms per second.

As the reaction rate increases with the temperature (law of Arrhenius), temperatures increase quickly resulting fi nally in a so - called thermal explosion. This can be characterized by an adiabatic temperature rise and a time until the explosion sets in, the so - called time to maximum rate under adiabatic conditions ( TMR ad ).

For reactions of n th order, which generate a large amount of heat, the time to maximum rate can be described by the following equation:

adTMR T = RT

Ecq Ta

p( )′( )

2

(15.3)

where R is the universal gas constant and E a the activation energy.


The time until the system explodes is a function of the initial heat release rate q ′ and thus of the temperature (Figure 15.1 ). Note that in the beginning, the temperature increase rate is only moderate. The TMR ad is therefore related to the probability of an incident. At very long times, countermeasures may be taken, for example, discharging the container or fi lling it with water. A very short time, however, does not allow any action to be taken, and the thermal explosion cannot be avoided.

Critical Conditions

Critical heat r elease rate The adiabatic case is the worst - case assumption. Any real system loses heat to its surroundings either by convection, in the case of liquids, or by conduction, in solids. A steady temperature will be obtained when the heat release rate of the reaction equals this heat loss rate, which is the defi nition of the critical heat release rate q ′ crit . Any heat release rate higher than this will lead to heat accumulation, to a temperature increase, and fi nally to a thermal explosion.

0

10

20

30

40

50

60

70

80

0 10 20 30 40 50 60 70

Time [min]

Tem

pera

ture

[°C

]

t

t/2

t/4

t/8

Figure 15.1. Temperature as function of time using the assumption that a 10 ° increase in temperature doubles the reaction rate. Under adiabatic conditions, this produces a thermal explosion after a defi ned time, as described in the section Probability: Time to Maximum Rate.


Critical l ayer t hickness In solids, the dominant heat transport mechanism is conduction. For each heat release rate, there is a critical layer thickness or critical radius r crit of the bulk, above which the decomposition heat can no longer be dissipated (Equation 15.4 ). For a given shape of the bulk, the radius will defi ne the volume.

critr

RT

E q Ta

= ∂⋅ ⋅′( )⋅

2 λρ

(15.4)

Critical t emperature As the rate is related to the temperature via Arrhenius law, there is also a critical temperature for a given bulk volume. Details can be found in Gray and Lee (1967) . The critical temperature, T crit , is therefore a function of the layer thickness, r , and its physical pro perties (density ρ , thermal conductivity λ , geometry δ ), as well as of the macrokinetics of the decomposition characterized by ( q ′ ( T ), E a ). Figure 15.2 shows a typical dependency of the critical radius of the temperature.

Many methods originally developed by trial and error use, in fact, the concept of critical temperatures (see below).

1.00E-03

1.00E-02

1.00E-01

1.00E+00

0 50 100 150 200Temperature [°C]

Crit

ical

rad

ius

[m]

unstable, thermal explosion

stable, no thermal explosion

Figure 15.2. Dependence of the critical radius r of the temperature T for a typical reaction as described in the text. Combinations of T and r in the overcritical region will lead to a thermal explosion; in the undercritical region, the system will be stable.


Autocatalysis

The concepts described above rely on the assumption that the heat release rate does not depend on the conversion. There are, how-ever, many cases in which the heat release rate initially increases with increasing conversion, reaches a maximum, and then decreases again. This behavior is called formal autocatalysis or, in short, autocatalysis .

For such cases, the equations previously discussed for the TMR ad and the critical layer thickness can be applied only with caution. It is therefore important to identify this type of formal reaction. However, using temperature - programmed thermoanalytical measurements for this is not obvious and requires experience: Bou - Diab and Fierz (2002) describe an identifi cation approach.


Differential scanning calorimetry (DSC) is often used either as a screening tool or to establish the thermal kinetics of a decomposition. Here, the change in heat fl ow as function of the oven temperature is recorded.

Becasue starting materials and products may be volatile, correct results are obtained only by using closed and pressure - tight crucibles. Measurements in which the samples are allowed to lose mass can show quite different behavior from those made in closed crucibles. An example describing this situation is given for saccharose in the Comparison of Open and Closed Measurement Methods section below. Typically, pressure - resistant gold - coated 40 μ l crucibles can be used, which can withstand 400 ° C/220 bars.

Screening

From one temperature - programmed run, both the reaction and decom-position energy and its temperature range can be deduced (ASTM E537 - 98 ).

As often is the case in calorimetry, the determination of the baseline is diffi cult because, for reactions, the recorded signals usually cover a broad temperature range. Interpolation of the baseline over such a broad range and therefore determination of the decomposition potential


can be quite diffi cult. To cool down the sample after a run and to perform a second scan without removing the sample crucible from the sensor helps establish the baseline.

Comparison of Open and Closed Measurement Methods

Figure 15.3 shows a typical thermogravimetric trace of sugar (saccha-rose). Thermogravimetric analysis is based on mass loss of the sample; the sample crucible is open to the atmosphere and thus the method is called an open method. At 220 ° C, there is a sudden decrease in mass followed by a gradual mass loss until, at 450 ° C, all the sample has evaporated. At the same time, the instrument used records a qualitative heat fl ow signal, which indicates a peak heat release rate at 350 ° C. The same substance measured in DSC in a closed crucible shows a quite high decomposition potential at lower temperatures (240 ° C) (Figure 15.4 ).

Estimation of q ′ ( T )

The detection limit of a modern DSC apparatus is about 1 W/kg. If this value is set equal to q ′ crit and used to calculate the critical radius r crit in Equation 15.4 , a value of about 0.1 m results, which is equivalent to a cubic container of approximately 8 L, depending on the assumptions

-20

0

20

40

60

80

100

120

0 50 100150

200250

300350

400450

500

Temperature [°C]

Mas

s lo

ss in

%

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

Del

taT

[K]TGA

SDTA

Figure 15.3. Saccharose measured in an open crucible at 4 K/min (thermogravimetric analysis) as an illustration of the difference between using either open or closed crucible. Shown is the thermogravimetry TGA signal (mass loss in percentage as function of temperature) and the qualitative DTA signal (SDTA: Δ T sample, oven).


made. In other words, lower heat release rates that may lead to a thermal explosion in a bigger volume may remain undetected.

Extrapolation of heat release rates to lower temperatures is thus often necessary. A conservative estimate (that is an estimate giving high values) for q ′ at lower temperatures can be obtained as follows,

Once a baseline is drawn, the deviation of the signal from the base-line at any temperature T is proportional to the heat release rate q ′ ( T ). If this is determined at the beginning of the signal, the infl uence of the conversion can be neglected, and q ′ ( T ) can be used as reference value (Figure 15.5 ). In practice, a value of 20 W/kg for q ′ ( T ) is a good com-promise between a value too small to be measured and one so high that there is already noticeable conversion.

To estimate heat release rates at other temperatures, a value for the activation energy E a is needed. This is generally not known a priori. To overcome this problem, a very low value of the activation energy, for example 50 kJ/mol, can be used, which in the case of real decom-positions is practically never encountered.

This can now be used to calculate a too - short and thus conservative value of the TMR ad (Figure 15.6 ) or of the critical radius r crit . If accept-able TMR ad values or critical volumes are then obtained at the desired temperature, no further action has to be taken. If not, more refi ned kinetic methods are needed.

Thus, from one measurement both the severity Δ T ad and the prob-ability of a runaway TMR ad can be estimated. This approach is in practical use in many laboratories and has been verifi ed theoretically (Keller et al. 1997 ). Also, by comparing actual adiabatic experiments to the results of the described method, it has been shown that the results

-1

-0.5

0

0.5

1

0 50 100 150 200 250 300 350 400

Temperature [°C]

Sig

nal [

W/g

]

Figure 15.4. DSC measurement of saccharose at 4 K/min in a closed pressure - resistant crucible. Note that the decomposition occurs at lower temperatures than in Figure 15.3 .

0

20

40

60

80

100

120

140

Hea

t rel

ease

rat

e [W

att/k

g]

0 50 100 150 200 250 300

Temperature [°C]

20 Watt/kg

Figure 15.5. Estimation of q ′ (T) values from a DSC measurement. Shown is a sche-matic DSC thermogram and the determination of the heat release rate at small conversion.

Figure 15.6. Infl uence of different activation energies on the safety margin of the extrapolated TMR ad . A measured heat release rate (15 W/kg at 160 ° C, upper right) can be used to extrapolate the heat release rate corresponding to a TMR ad of 24 h using a high activation energy (lower curve) or a low activation energy (upper curve). Extrapolated temperatures of 100 ° C and 50 ° C, respectively, result.

359


so obtained in all studied cases were on the conservative side (Pastr é et al. 2000 ).

Some words of caution are necessary, however:

• The point of the curve where 20 W/kg is reached should correspond to a conversion of less than 10%.

• The method should not be used for extrapolations to higher temperatures.

• It works best when the detectable beginning of the signal lies below 250 ° C.

• It depends on a good defi nition of the baseline. Especially strongly curved baselines will make the determination of the heat release rate unreliable at small conversion.

• One should not extrapolate across a melting point, because decom-position mechanisms in the solid state can be very different from those in the liquid state.

Isoconversional Methods

Isoconversional methods deliver formal kinetic information by using a set of measurements where the heat release rate is measured at dif-ferent temperatures and always at the same conversion. Once the kinetic information is known, it can be used to calculate the heat release rate and the conversion at different conditions, such as at fi xed temperatures or under adiabatic conditions.

Such an “isoconversional” set can consist of measurements at dif-ferent but constant heating rates. Whereas simple methods are easy to use but limited to n th - order reactions (ASTM E698 ), the more advanced methods depend on sophisticated computer algorithms (Opfermann and H ä drich 1995 ; Roduit 2000 ), which are outside the scope of this chapter.

Although the isoconversional method can be considered to be state of the art, it is not always possible to apply it. For example, in cases where the product melts and decomposes in the liquid state, it is often not possible to determine the decomposition kinetics in the solid state.

Alternatively, one can use a set of measurements at different but constant temperatures, where the signal is recorded as a function of time. This approach works best with kinetics of n th - order, where the highest heat release rate occurs at the beginning of the isothermal


phase. An Arrhenius graph of the maxima of the heat release rate q ′ is then plotted as a function of the temperatures T , and from this reference the values for the heat release rate and the activation energy can be obtained. It is assumed that at the maximum of the heat release rate there is not yet any signifi cant conversion. This method is easy to understand and to use; however, the temperature range and therefore the number of points in the Arrhenius graph is limited: (1) For isother-mal measurements at high temperatures, there is some conversion during heatup, so the assumption of zero conversion therefore may not be true; and (2) at low temperatures, measurement time can be very long, as the baseline is known only after complete conversion.

High - Sensitivity Calorimetry

High - sensitivity calorimetry (Suurkuus and Wads ö 1982 ) can be used to avoid the cumbersome extrapolation of heat release rates to lower temperatures. In these instruments, heat release rates as low as 0.001 – 0.03 W/kg can be measured directly. The method works at constant temperatures between 30 ° and 80 ° C, and measurements usually take several days to perform.

Adiabatic Measuring Methods

In an adiabatic calorimeter, there is no heat exchanged with the sur-roundings. Any heat produced in a system will therefore remain inside the system and produce a temperature rise. This can be measured, and once the heat capacity of the system is known, the heat of reaction can be determined. So at least in principle, one could put the decomposing substance in such a calorimeter and obtain both the adiabatic tempera-ture rise and the TMR ad .

Dewar Vessels

The use of adiabatic calorimeters seems to be an attractive choice, and the type most commonly used is the Dewar vessel (Rogers 1989 ). These are quite well insulated, but a 500 - ml vessel fi lled with a liquid still has a heat loss of typically 0.04 W/kg/K. Heat release rates of less than 0.2 – 0.4 W/kg will in this case not lead to self - heating.


Dewar vessels are usually made of glass and are not pressure - resistant. Gases and vapors must be allowed to escape from the system, and because this is normally an endothermic process, it also will contribute to the heat loss. The sensitivity of such an arrangement can be improved (1) by minimizing the temperature difference between Dewar and oven by adapting the temperature of the oven to that of the Dewar and (2) by placing the Dewar vessel in an autoclave to sup-press the evaporation of gases and vapors (Grewer 1994 ). The use of containment is also advisable for reasons of safety and industrial hygiene.

This improvement requires the use of rather sophisticated experi-mental facilities, and especially in the case of autoclaves, a room with concrete walls and with remote control should be used.

Accelerating Rate Calorimeter

The accelerating rate calorimeter was fi rst described by Townsend (Townsend and Tou 1980 ). In this instrument, it is possible to measure both the TMR ad and the adiabatic temperature rise with a relatively small sample of a few grams. A small pressure - resistant container is fi lled with the sample and placed in an oven at a temperature closely matches that of the sample container. There is therefore no heat exchange between sample and oven, and heat generated by a reaction will produce a proportional temperature rise. Despite the small sample size, the instrument is quite sensitive. It can detect approximately 0.3 W/kg, which corresponds to a heat rate of approximately 0.01 K/min or 0.6 K/h, and its sensitivity corresponds to that of a 400 - ml Dewar vessel.

The proportionality between the specifi c heat of reaction Q r ′ 4 and Δ T ad or the specifi c heat production q ′ and the rate of temperature rise d T /d t is the specifi c heat capacity of the system. The heat produced by the sample will heat not only the sample itself but also the sample container. To obtain TMR ad and Δ T ad values of the substance itself, the measured values TMR meas and Δ T meas must be corrected with a factor Φ as follows:

TMR TMR

adcorr ad

meas

=Φ

(15.5)


and

Δ Δ ΦT Tadcorr

admeas= ⋅ (15.6)

where

Φ = +

××

1m cm cb p b

s p s

,

, (15.7)

and m is mass, c p is specifi c heat capacity, and subscript b and s mean sample container and sample, respectively. The correction for the TMR ad is valid for reactions of 0th order. At small conversion, n th - order reactions can be treated as being of 0th order, as conversion can be neglected.

Reactions with Oxygen

Tests for the determination of the self - ignition temperature simulate a dryer, where the powder sample is in close contact with hot air. Different test arrangements are in use, which can be classifi ed accord-ing to how oxygen is delivered to the sample: by forced convection or diffusion (Grewer 1971 ). In all these tests, the temperature of the hot air is either held constant or increased continuously, and the tempera-ture difference between the sample and either an inert reference or the surroundings is recorded. With methods using relatively big sample volumes, temperatures in the center of the sample can deviate consider-ably from the temperature of the surrounding air and can reach over-critical values. These instruments will therefore not give quantitative heat release rates, but will be more suited for the detection of critical temperatures.

Screening Test

Currently, a widely used screening test apparatus, which is described by Grewer (1971) , uses preheated air that fl ows through both a sample and a reference channel containing the respective samples in small wire baskets. An inert substance such as graphite is used as reference.


Determination of Self - Ignition Temperature

In the European Union, a testing method of the diffusion type is in offi cial use (method A16 of the European Commission Joint Research Centre ). Here, the sample is placed in a wire basket surrounded by hot air. The temperature of the oven in which the sample reaches 400 ° C due to self - ignition is called the self - ignition temperature .

Applications

Formation of Hot Spots in Dryers

Many solid food products are produced by spray - drying of water - based solutions. Such a solution is sprayed into a stream of hot air. The water is evaporated, and the solute is converted to a powder. Examples are the production of powdered milk or coffee whitener.

Reactions with oxygen can lead either to product deterioration or to smoldering if there are deposits. Deposits not only occur in the dryer itself but also in equipment downstream, for example, in fi lters. As discussed previously, if the heat release rate due to the oxidation in such a product layer is overcritical, the formation of hot spots that can trigger a dust explosion is unavoidable. In general, the self - ignition temperature should therefore be determined.

Storage and Hot Discharge

Sometimes the product is discharged from a dryer to drums or contain-ers while still hot. In a dryer, heat produced by an ongoing decomposi-tion reaction can be removed by convection because the product is agitated. In a container, however, this agitation is lacking, and heat is removed by conduction only. This is a much less effi cient mechanism of heat transfer. The product can therefore self - heat, the self - ignition temperature can be reached, and a fi re can occur.

If the decomposition kinetics are known, it is possible to calculate the critical temperature for a given container size. Alternatively, one can establish a temperature limit using a series of isothermal Dewar experiments by starting at a relatively high temperature where a signal is observed and then lowering the temperature in steps of 10 K until no signal is observed. From this temperature a safety margin, for


example 50 K, is deduced. This gives a conservative estimate for the allowed discharge temperature.

Prevention of Molasses Incidents

Several incidents where big molasses reservoirs burst are recorded, for example, the 1919 Great Molasses Disaster in Boston, Massachusetts, in which a large tank burst, fl ooding the streets with molasses and killing several people. The bursting could have been caused by various reasons. One reason is certainly the exothermic decomposition in com-bination with the formation of carbon dioxide (Strecker degradation).

The DSC thermogram of the molasses studied in our laboratories shows a decomposition signal between 120 ° C and 250 ° C, with an energy of several hundred kilojoules per kilogram. Isothermal mea-surements in the thermal activity monitor showed a heat release rate of 0.004 W/kg at 50 ° C and 0.06 W/kg at 75 ° C, which gave an activa-tion energy of about 100 kJ/Mol. The critical radius at 30 ° C calculated by Equation 15.4 and using the above - determined kinetics is fairly low, approximately 3.5 m, which is equivalent to a capacity of 100,000 l and therefore comparable with the size of actual reservoirs. These fi ndings are confi rmed in the literature (Platje, Wittenberg, and Timmermans 2006 ). Heating of molasses in the reservoir to reduce the viscosity and to facilitate pumping should therefore be avoided.

Transport Safety

The UN recommendations for the transport of dangerous goods (UNECE 2003 ) require a chemical to be tested for self - reactivity (Class 4.1) if it releases more than − 300 kJ/kg. It is considered to be self - reactive if a 50 - kg package has a self - accelerating decomposition temperature (SADT) less than 75 ° C. The SADT is defi ned as Δ T = T i − T a ≥ 6 K, where T i is the temperature of the package and T a is the temperature of the environment.

According to the recommendations, a 50 - kg package can be substi-tuted by a Dewar vessel with a specifi c heat loss of 0.08 W/kg/K.

For screening purposes, the specifi c heat release rate can thus be estimated at 81 ° C using the method outlined in the Estimation of q ′ ( T ) section. If the specifi c heat release rate is higher than 0.48 W/kg, a full determination of the SADT must then be made using either the heat


accumulation storage test (HAST) H.4, based on a 500 - ml Dewar vessel, or the isothermal storage test (IST) H.3, based on isothermal measurements. Depending on the outcome of these tests, transport restrictions apply; for example, packages have to be cooled during transport or air transport is not allowed. Typical curves of food prod-ucts, which currently would probably have to be classifi ed as class 4.1, have been published previously (Raemy and Lambelet 1982 ).

Conclusion

Food, especially carbohydrate - and protein - based food, can undergo highly exothermal decompositions. Unlike other chemicals, food itself is by defi nition not toxic; nevertheless, the consequences of an accident involving bulk food can be devastating.

It is possible, however, to assess the risk of such decompositions using the same principles as for process chemistry. If these principles are properly applied and the stability data correctly determined, safe conditions for handling can be established and accidents in the food industry prevented.

References

ASTM Standard E 698 . 2005 . Test Methods for Arrhenius Kinetic Constants for Thermally Unstable Material . ASTM International: West Conshohocken, PA. Retrieved from: www.astm.org .

ASTM Standard E 537 - 98 . 2007 . Standard Test Method for Assessing the Thermal Stability of Chemicals by Methods of Thermal Analysis . ASTM International: West Conshohocken, PA. Retrieved from: www.astm.org .

Bartknecht , W. 1981 . Explosions. Course Prevention Protection . Springer : Berlin . Bou - Diab , L. and Fierz , H. 2002 . Autocatalytic decomposition reactions, hazards, and

detection . J Hazard Mater , 93 ( 1 ): 137 – 146 . European Commission Joint Research Centre, Institute for Health and Consumer

Protection . Directive 67/548/EEC, Annex V, Method A 16. Retrieved from: http://ecb.jrc.it/testing-methods .

Gray , P. , Lee , P.R. editors. 1967 . Thermal Explosion Theory, Oxidation and Combustion Reviews , 2nd edition . Elsevier : Amsterdam .

Grewer , T. 1971 . Zur Selbstentz ü ndung von abgelagertem Staub . Staub - Reinhaltung der Luft , 31 ( 3 ): 97 – 101 .

Grewer , T. 1994 . Thermal Hazards of Chemical Reactions, Industrial Safety Series , Vol. 4 . Elsevier : Amsterdam .


Keller , A. , Stark , D. , Fierz , H. , Heinzle , E. , and Hungerb ö hler , K. 1997 . Estimation of the time to maximum rate using dynamic DSC experiments . J Loss Prevent Process Ind , 10 : 31 – 41 .

Opfermann , J. and H ä drich , W. 1995 . Prediction of the thermal response of hazardous materials during storage using an improved technique . Thermochim Acta , 263 : 29 – 50 .

Pastr é , J. , W ö rsd ö rfer , U. , Keller , A. and Hungerb ö hler , K. 2000 . Comparison of different methods for estimating TMR ad from dynamic DSC measurements with ADT 24 values obtained from adiabatic Dewar experiments . J Loss Prevent Process Ind , 13 : 7 – 17 .

Platje , T. , Wittenberg , A. , and Timmermans , A. 2006 . Study of the “runaway behav-iour” of technical sucrose solutions . Zuckerindustrie , 131 ( 4 ): 231 – 238 .

Raemy , A. and Lambelet , P. 1982 . A calorimetric study of self heating in coffee and chicory . J Food Technol , 17 : 451 – 460 .

Roduit , B. 2000 . Computational aspects of kinetic analysis. Part E: The ICTAC Kinetics Project — numerical techniques and kinetics of solid state processes . Thermochim Acta , 355 : 71 .

Rogers , R.R. 1989 . The advantages and limitations of adiabatic Dewar calorimetry in chemical hazard testing . Plant Operations Progress , 8 : 109 .

Stoessel , F. 2008 . Thermal Safety of Chemical Processes: Risk Assessment and Process Design . Wiley - VCH : Weinheim .

Suurkuus , J. and Wads ö , I. 1982 . A multichannel microcalorimetry system . Chem Scripta , 20 : 155 – 163 .

Townsend , D.I. and Tou , J.C. 1980 . Thermal hazard evaluation by an accelerating rate calorimeter . Thermochim Acta , 37 : 1 – 30 .

UNECE Transport Division . 2003 . International Recommendations on the Transport of Dangerous Goods, Manual of Test and Criteria , 4th edition . UNECE : New York .

369

Index

Accelerating rate calorimeter, 362–63

Activation enthalpy, protein heat-induced transformations with, 129

Adiabatic measurement methodsaccelerating rate calorimeter,

362–63Dewar vessels, 361–62food-processing safety with,

361–63Adiabatic temperature rise, 353ADSC. See Alternating DSCAggregation

DSC technique v. vessel/heating mode with, 28t

globular proteins in bulk phase system, 124–29, 126f–128f

heat effects of, 88heating mode with, 36–37, 37fprotein postdenaturation, 110–12

Alcohols, protein denaturation affected by, 99–100, 101f

11S globulin, 99–100, 101fSetschenow equation for, 100

Alfalfa, 93Alginate, denaturation temperature

of β-lactoglobulin with, 110

Alternating DSC (ADSC), food-processing design with, 204

AMF. See Anhydrous milk fatAmpoule mixing vessel, mixing and

reaction heat fl ux microcalorimeter with, 30, 31t

Anhydrous milk fat (AMF)Avrami plots from, 139fcalorimetric parameters observed

with, 136theat-induced transformations

with, 133–41, 135f, 136t, 137f, 139f, 139t, 140f, 184–87, 186f, 187f

heating/cooling curves, 135fisothermal curves, 137fprotein-stabilized, 135fsurfactant added, 135f, 136t, 137f

Antibiotics, 153–55, 153f, 155fApple, heat capacity for, 36tArabic gum, denaturation

temperature of 11S globulin with, 106t

Autocatalysis, 356Autoclave, 54, 54f, 55f, 57Avrami equation, oil-in-water

emulsions with, 138–39, 139f, 139t

370 Index

Bacillus megaterium, DSC analysis of, 149

BacteriaBacillus megaterium DSC

analysis of, 149calorimetry with growth of, 43Citrobacter freundii DSC

analysis of, 149Clostridium perfringens DSC

analysis of, 149–53, 151fresults for, 150–52, 151fsample preparations for,

149–50DSC analysis of foodborne,

147–64, 151f, 153f, 155f, 157f, 160f, 162t

antibiotics’ effect on, 153–55, 153f, 155f

cold shocking, 148heat shocking, 148, 151


endothermic/exothermic effects of, 19t

Escherichia coli DSC analysis of, 148, 155–58, 157f

erythromycin treatment of, 154–55, 155f

heat inactivation parameters of, 159–60, 160f

nonthermal treatment of, 162–63, 162t

food-processing’s effect on, 10–11

food-processing treatment evaluation by DSC for, 158–64, 160f, 162f

antimicrobials in, 163–64heat inactivation parameters of

bacteria in, 158–61, 160f, 162f

HHP in, 161

nonthermal treatment of bacteria in, 161–63, 162f

hydrostatic pressure resistance of, 44–45, 44f

inactivation of, 10Lactobacillus plantarum DSC

analysis of, 155–58, 157fListeria monocytogenes DSC

analysis of, 149–53, 151fantibiotics’ effect on, 153–54,

153fheat inactivation parameters of,

159results for, 152–53, 152fsample preparations for,

149–50Mycoplasma laidlawii DSC

analysis of, 149Staphylococcus aureus

nonthermal treatment, 162–63, 162t

Batch high-pressure vessel, mixing and reaction heat fl ux microcalorimeter with, 29, 31t

Batch mixing vessel, high sensitivity heat fl ux calorimeter with, 27, 28t

Batch standard vessel, mixing and reaction heat fl ux microcalorimeter with, 29, 31t

Benzene, transitiometry verifi cation test using, 322, 323f

Bindingdata quantifi es high-affi nity,

75–77mixing and reaction calorimetry

with, 41–42processes, protein in dilute

solution with, 78, 79, 80fBlank test heat fl ow equation, 32

Index 371

Bovine β-lactoglobulin, 94–95Bovine serum albumin (BSA),

protein denaturation affected by, 104

Broad beans, 11S globulin from, 99–100, 101f, 105

BSA. See Bovine serum albuminBulk phase system, denaturation-

aggregation of globular proteins in, 124–29, 126f–128f

Butter, heat capacity for, 36tButyric acid, melting point of,

171f

Cabbage, heat capacity for, 36tCalibration, 23–25, 24f, 25f

Calvet type calorimeter, 23, 206

food-processing design, 206heat calibration procedure for

HP-DSC, 60f, 61–63, 62fhigh pressure calorimetry, 314HP-DSC, 57–63, 60f, 62fJoule effect in, 23–24, 24f, 25fMICROCALIX, 177necessity for HP-DSC, 57parameters for, 58

heating rate, 58pan type, 58sample mass, 58temperature, 58

reference substances for, 58indium, 58, 60, 61lead, 58tin, 58, 59zinc, 58

scanning transitiometry, 322–23table of corrections for, 57temperature calibration procedure

for HP-DSC, 58–61uncertainty with HP-DSC, 58

Calorimeteraccelerating rate, 362–63symmetrical, two-chamber,

17–18Calorimetry. See also Calvet type

calorimetry; Differential scanning calorimetry; Heat fl ux calorimeters; Heat fl ux microcalorimetry; High pressure calorimetry; High pressure differential scanning calorimetry; High-sensitivity calorimetry; High sensitivity heat fl ux calorimeter; Isothermal calorimetry; Isothermal solution calorimetry; Isothermal titration calorimetry; Microcalorimetry; Mixing and reaction calorimetry; Mixing and reaction heat fl ux microcalorimeter; Pressure calorimetry

advantages for using, 7applications of, 15–45under controlled relative

humidity, 45food dehydration understood

with, 289–309calorimetric glass transition

measurement for, 293–96, 294f, 296f

dielectric and mechanical relaxations with, 296f, 297–98

freeze-drying for, 290, 306–7freezing in, 301–3, 302f, 303fglass transition and stability of,

307–8, 308fphase and state transitions of,

290, 292–93, 293f

372 Index

Calorimetry (continued)spray-drying for, 290, 305–6,

306fstate diagrams with, 303–7,

304fthermal analysis in, 298–301,

299f, 300ffood industry interest in, 226food-processing design in, 202–6

alternating DSC, 204Calvet type calorimetry, 203,

206differential scanning

calorimetry, 202–6differential thermal analysis,

203dynamical mechanical analysis,

225dynamical mechanical thermal

analysis, 225methods, 205–6modulated DSC, 204samples, 206techniques, 203–5, 204f

food-processing safety evaluated with, 351–66, 354f, 355f, 357f–359f

adiabatic measurement methods for, 361–63

applications for, 364–66concepts for, 352–56, 354f,

355fcritical conditions in, 354–56,

355fcritical heat release rate in,

354–55critical temperature, 355–56,

355festimation of q’(T) in, 357–60,

359fformation of hot spots in

dryers, 364

high-sensitivity calorimetry in, 361

isoconversional methods in, 360–61

open v. closed measurement methods in, 357, 357f, 358f

prevention of molasses incidents, 365

reactions with oxygen in, 363–64

screening in, 356–57storage and hot discharge,

364–65transport safety, 365–66

isothermal, 38isothermal performance of, 19isothermal solution, 220methods with food using, 5–13parameters of, 8

interpretation of overlapping peaks, 8

magnitude of heat fl ow, 8moisture loss, 8time scale, 8

pressure, 43–45, 44fscanning mode, 35–36, 44solution, 218step heating in, 40, 40fsuitability for food of, 52ultrasensitive to proteins, 8

Calvet principle, 22–23, 22f, 23f, 26Calvet type calorimetry, 22–23, 22f,

23f, 203calibration of, 23, 206food-processing design with, 203,

206Capric acid, melting point of, 171fCaproic acid, melting point of, 171f,

172fCaprylic acid, melting point of,

171fCarbohydrates

Index 373

C80 technique v. vessel/heating mode with, 31t

cereal with, 12endothermic/exothermic effects

of, 19tgelatinization of starch-water

systems, 207glass transition with, 294hydrophilic component of,

291thermal analysis of cereal

nonstarch, 276–78, 277fthermal behavior of food

constituents in, 206–8, 207f, 208f

Carboxymethylcellulose, denaturation temperature of 11S globulin with, 106t

Carp, heat capacity for, 36tκ-Carrageenan

denaturation temperature of 11S globulin with, 106t

denaturation temperature of β-lactoglobulin with, 110

λ-Carrageenandenaturation temperature of 11S

globulin with, 106tdenaturation temperature of

β-lactoglobulin with, 110Carrots, isothermal traces at

temperatures for, 39fCaseins, 122–23CB. See Cocoa butterCellobiose, calorimetric curves of,

208fCentre National de la Recherche

Scientifi que (CNRS), 176Cereal, 12


Cereal processing, thermal analysis to design/monitor, 265–85

nonstarch carbohydrates, 276–78, 277f

process applications, 278–85, 280f–284f

proteins, 272–76, 274f, 275fstarch, 268–72, 289f–272f

Chaotropic salts (Salting-in salts), 95

ChocolateC80 technique v. vessel/heating

mode with, 31tDSC technique v. vessel/heating

mode with, 28tCitrobacter freundii, DSC analysis

of, 149Closed measurement method, 357,

357f, 358fClostridium perfringens

DSC analysis of, 149–53, 151fresults for, 150–52, 151fsample preparations for, 149–50

CNRS. See Centre National de la Recherche Scientifi que

Cocoa butter (CB)DSC and XRD with, 179–84,

180f, 183fMICROCALIX for, 182–84, 183fpolymorphism, 181–82fpolymorphism of 1,2-dipalmitoyl-

3-oleoylglycerol, 182–84, 183f

Coffee, C80 technique v. vessel/heating mode with, 31t

Cold denaturation, protein in dilute solution with, 75

Cold shocking, 148Complete reaction, test for, 255Cream, heat capacity for, 36tCritical conditions, food-processing

safety with, 354–56, 355fheat release rate, 354–55temperature, 355–56, 355f

374 Index

CrystallizationDSC technique v. vessel/heating

mode with, 28theating mode with, 36isothermal, 39lard, 190, 191f, 191tlipids, 210–11milk fat, 189–90oil-in-water emulsions, 132–36,

135f, 136twater in pork muscle with, 327f

CSC, 21

Dairy, heat capacity for, 36tDebye-Höckel approximation, 98,

99tDehydration. See Food dehydrationDenaturation

cold, protein in dilute solution with, 75

defi ned, 122DSC technique v. vessel/heating

mode with, 28tglobular proteins in bulk phase

system, 124–29, 126f–128f11S globulin, 89–92, 91f

alcohol’s effect on, 99–100, 101f

different pH values in, 91f, 92polysaccharides’ effect on,

105–6, 106t, 110salt’s effect on, 96–98, 97f, 99ttwo-state model to analyze, 89

heat effects of, 88heating mode with, 36–37, 37fKunitz inhibitor, polysaccharides’

effect on, 107–10, 108fβ-lactoglobulin, 125methodological approaches to

study, 89of protein, 87–113, 91f, 97f, 99t,

101f, 103f, 106t, 108f

effects of alcohols on, 99–100, 101f

effects of odorants on, 102–4, 103f

effects of pH on, 89–95, 91feffects of polysaccharides on,

104–10, 106t, 108feffects of salts on, 95–99, 97f,

99treversibility of, 123two-state model of, 123

Dewar vessels, 361–62Dextran, denaturation temperature

of 11S globulin with, 106t, 108f

Dextran sulfate, 106t, 108fDifferential scanning calorimetry

(DSC), 6–7, 9, 16, 265Bacillus megaterium analysis by,

149calibration of, 23–25, 24f,

25fCalvet principle with, 22–23, 22f,

23f, 26Calvet type of, 22–23, 22f, 23f,

203, 206calibration of, 23, 206effi ciency ratio of, 23fschematic of, 22f

Citrobacter freundii analysis by, 149

Clostridium perfringens analysis by, 149–53, 151f

results for, 150–52, 151fsample preparations for,

149–50cold denaturation with, 75data quantifi es high-affi nity

binding with, 75–77assumptions of, 75concentration in, 77equilibrium in, 76–77

Index 375

hydrogen ion buffer selection in, 77

purity in, 77two-state, reversible transitions

in, 76–77effi ciency ratio of fl at-shaped,

21fEscherichia coli analysis by, 148,

155–58, 157ferythromycin treatment of,

154–55, 155fheat inactivation parameters of,

159–60, 160fnonthermal treatment of,

162–63, 162tfoodborne bacteria analysis by,

147–64, 151f, 153f, 155f, 157f, 160f, 162t

antibiotics’ effect on, 153–55, 153f, 155f

cold shocking, 148heat shocking, 148, 151

food-processing design in, 202–6methods, 205–6samples, 206techniques, 203–5, 204fwith XRD, 225

food-processing safety with, 355f, 356–61, 357f–359f

estimation of q’(T) for, 357–60, 359f

high-sensitivity calorimetry for, 361

isoconversional methods for, 360–61

open v. closed measurement methods for, 357, 357f, 358f

screening for, 356–57food-processing treatment

evaluation by, 158–64, 160f, 162f

antimicrobials in, 163–64

heat inactivation parameters of bacteria in, 158–61, 160f, 162f

HHP in, 161nonthermal treatment of

bacteria in, 161–63, 162fglass transition with, 294fheat fl ux type of, 20–21, 26–30,

27f, 28theating’s role in, 88high pressure, 51–64, 54f–56f,

60f, 62fapplications of, 63calibration of, 57–63, 60f, 62fconstruction of, 53–57,

54f–56fLactobacillus plantarum analysis

by, 155–58, 157fListeria monocytogenes analysis

by, 149–53, 151fantibiotics’ effect on, 153–54,

153fheat inactivation parameters of,

159results for, 152–53, 152fsample preparations for,

149–50microcalorimetry v., 16, 19–25,

20f–25fheat fl ux microcalorimetry,

19–25, 20f–25fMycoplasma laidlawii analysis

by, 149power compensated type of,

20–22protein in dilute solution with,

68–77, 71fequations, 70–74, 71fheat capacity change origins

for, 74information content, 68–69instrumentation, 69–70

376 Index

Differential scanning calorimetry (DSC) (continued)

simulated DSC thermogram of, 70, 71f

van’t Hoff enthalpy change, 72–74

schematic of plate-shaped sensor for, 20f

schematic representation of, 299fsensor plate thickness in

effi ciency of, 21fStaphylococcus aureus

nonthermal treatment with, 162–63, 162t

starch analysis with, 268–72, 289f–272f

starch gelatinization by heat monitored with, 342, 343f

two types of, 20X-ray diffraction with, 169–94,

171t, 172t, 173f, 177f, 180f, 183f, 186f, 187f, 189f, 191f, 192f

applications for, 179–93, 180f, 183f, 186f, 187f, 189f, 191f, 192f

cocoa butter in, 179–84, 180f, 183f

lard in, 190–93, 191f, 192fMICROCALIX using, 170,

176–79, 177f, 180f, 183f, 186f, 187f, 189f, 191f, 192f

milk fat in, 184–90, 186f, 187f, 189f

results using, 179–93, 180f, 183f, 186f, 187f, 189f, 191f, 192f

triacylglycerols in, 169–76, 171t, 172t, 173f

Differential thermal analysis (DTA), 53, 173

food-processing design with, 203

Dilute solution, calorimetry of protein in, 67–84, 71f, 80f, 83f

Dilute systems, 10Dissolution, mixing and reaction

calorimetry with, 41DMA. See Dynamical mechanical

analysisDMTA. See Dynamical mechanical

thermal analysisDrying

freeze-, 290, 306–7hot spots with, 13spray-, 290, 305–6, 306f

DSC. See Differential scanning calorimetry

DTA. See Differential thermal analysis

Dynamical mechanical analysis (DMA)

food dehydration with, 296f, 297–98

food-processing design in, 225glass transition detected with,

296f, 297thermal analysis with, 266

Dynamical mechanical thermal analysis (DMTA)

food dehydration with, 296f, 297–98

food-processing design in, 225glass transition detected with,

296f, 297thermal analysis with, 266

Eggs, isothermal traces at temperatures for, 39f

Electromotive force (Emf), 23, 25Electron spin resonance (ESR),

301Emulsions

lipids, 214

Index 377

oil-in-water, 132–41, 135f, 136t, 137f, 139f, 139t, 140f

anhydrous milk fat, 133–41, 135f, 136t, 137f, 139f, 139t, 140f, 184–87, 186f, 187f

Avrami equation for, 138–39, 139f, 139t

crystallization in, 132–36, 135f, 136t

fat crystal growth in, 138Gompertz model for, 139–40,

139t, 140fice cream, 133kinetics of, 136–41, 137f, 139f,

139t, 140fmelting of fat droplets in,

132–36, 135f, 136ttriacylglycerols, 133whipped cream, 133

protein’s role in, 10Enthalpy, 265

activation, 129estimate of apparent denaturation,

112reaction, 255van’t Hoff enthalpy change,

72–74Entropy, protein heat-induced

transformations with, 129Enzymatic reactions, mixing and

reaction calorimetry with, 42, 42f, 43f

EnzymeC80 technique v. vessel/heating


mode with, 28tendothermic/exothermic effects

of, 19tErythromycin, Escherichia coli

treatment with, 154–55, 155fEscherichia coli

DSC analysis of, 148, 155–58, 157f

erythromycin treatment of, 154–55, 155f

heat inactivation parameters of, 159–60, 160f

hydrostatic pressure resistance of, 44–45


ESR. See Electron spin resonanceExothermic decomposition, 13

Fat. See also LipidsC80 technique v. vessel/heating



of, 19toxidative stability of, 211–12

Fatty acids, 170–73, 171t, 172t, 173f

crystallographic/energetic properties of, 172f

hexagonal, 172, 172f, 173fmelting point of, 171f, 172forthorhombic perpendicular, 172,

172f, 173ftriclinic parallel, 172, 172f,

173fFermentation, mixing and reaction

calorimetry with, 43Fish, heat capacity for, 36tFluid mixing vessel, high sensitivity

heat fl ux calorimeter with, 27, 28t

Foams, protein’s role in, 10Food dehydration, 289–309

calorimetric glass transition measurement for, 293–96, 294f, 296f

378 Index

Food dehydration (continued)dielectric and mechanical

relaxations with, 296f, 297–98

freezing in, 301–3, 302f, 303fglass transition and stability of,

307–8, 308fphase and state transitions of,

290, 292–93, 293fstate diagrams with, 303–7, 304f

freeze-drying, 290, 306–7spray-drying, 290, 305–6, 306f

thermal analysis in, 298–301, 299f, 300f

Food fl avorings, 89odorants with, 102

Food-processing design, 201–27food industry interest in

calorimetry for, 226related techniques for, 225

DMA, 225DMTA, 225DSC combined with XRD,

225safety aspects for, 217–18thermal analysis/calorimetry on,

203–6, 204fmethods, 205–6samples, 206techniques, 203–5, 204f

thermal behavior of food constituents in, 206–17, 207f, 208f, 210f, 213f, 215f, 216f

carbohydrates, 206–8, 207f, 208f

lipids, 208–14, 210f, 213fproteins, 214–16, 215f, 216fsugars, 206–8, 207f, 208fwater, 216–17

thermal behavior of raw/reconstituted food in, 217

thermodynamic parameters for, 218–25, 221f–223f

heat of combustion, 225heat of solution, 218–24,

221f–223fspecifi c heat, 224–25

Food-processing safetyadiabatic measurement methods

with, 361–63accelerating rate calorimeter,

362–63Dewar vessels, 361–62

applications for, 364–66formation of hot spots in

dryers, 364prevention of molasses

incidents, 365storage and hot discharge,

364–65transport safety, 365–66

calorimetry for, 351–66, 354f, 355f, 357f–359f

concepts with, 352–56, 354f, 355f

adiabatic temperature rise, 353autocatalysis, 356probability, 353–54, 354fseverity, 353time to maximum rate, 353–54,

354fcritical conditions with, 354–56,

355fheat release rate, 354–55temperature, 355–56, 355f

differential scanning calorimetry for, 355f, 356–61, 357f–359f

estimation of q’(T) with, 357–60, 359f

high-sensitivity calorimetry with, 361

isoconversional methods with, 360–61

Index 379

open v. closed measurement methods with, 357, 357f, 358f

screening with, 356–57reactions with oxygen in, 363–64

determination of self-ignition temperature for, 364

screening test for, 363Formal autocatalysis, 356Fourier transform infrared

spectroscopy, 11Free protein, denaturation


Freeze-drying, 290, 306–7Fruit, heat capacity for, 36t

Galactose, calorimetric curves of, 208f

Gas-fl ow vessel, mixing and reaction heat fl ux microcalorimeter with, 29, 31t

Gelatin, endothermic/exothermic effects of, 19t

Gelatin gels, frozen water ratio in, 326–29, 328f

GelatinizationDSC technique v. vessel/heating

mode with, 28theating mode with, 38starch, 12, 274, 278–79, 280f

calorimetric analysis by HPP of, 341–49, 343f, 346f

heat in, 342, 343fhigh pressure calorimetry on,

330–36, 332f–334f, 335tstorage of, 347–48thermodynamic data for, 335twheat, 344–47, 346f

starch-water systems, 207Gelatin molecules, 122

Gelation, heating mode with, 37–38Gibbs function, 267Glass transition, 290–92

behavior of lactose with, 300fcalorimetric measurement for,

293–96, 294f, 296fcooling/heating with, 295DSC technique v. vessel/heating

mode with, 28tDSC with, 294fGordon-Taylor equation with,

300mechanical/dielectric relaxations

in, 296f, 297stability of dehydrated materials

with, 307–8, 308fstudies referring to, 291sugar/carbohydrates with, 294two or more components with,

295Globulin, 7S, different pH values in

denaturation of, 93Globulin, 11S

alcohols effect on protein denaturation using, 99–100, 101f

broad beans, 99–100, 101f, 105denaturation of, 89–92, 91f

different pH values in, 91f, 92two-state model to analyze, 89

polysaccharides effect on protein denaturation using, 105–6, 106t, 110

β-glucosidase, ITC of binding inhibitors to, 80f

Gompertz model, oil-in-water emulsions with, 139–40, 139t, 140f

Gordon-Taylor equation, 300, 304Grapefruit, heat capacity for, 36tGuar gum, denaturation temperature

of β-lactoglobulin with, 110

380 Index

Heat calibration procedure, HP-DSC, 60f, 61–63, 62f

Heat capacityconstant pressure processes with,

30defi ned as ratio, 30determination, 30–35, 33f, 34f,

36tfoods in, 35, 36tliquids in, 34–35, 34ftemperature-scanning mode in,

31–33, 33ftemperature step mode in,

33–34DSC with, 6formula for heat fl ux with, 17

Heat fl owblank test heat fl ow equation, 32magnitude of, 8

Heat fl uxelectrical signal’s correlation

with, 24pork muscle crystallization with,

327fpork muscle thawing with, 326fpower dissipation’s correlation

with, 24pressure shift freezing with, 330fratio of measured to total, 20–21,

21fHeat fl ux calorimeters, 20–21,

26–30, 27f, 28thigh sensitivity, 26–27, 27f, 28t

batch mixing vessel for, 27, 28t

fl uid mixing vessel for, 27, 28t

mixing vessels for, 27, 27ftemperature control in, 26thermal conductive block of,

26mixing and reaction, 29–30, 31t

ampoule mixing vessel for, 30, 31t

batch high-pressure vessel for, 29, 31t

batch standard vessel for, 29, 31t

gas-fl ow vessel for, 29, 31tmembrane mixing vessel for,

29–30, 31tmixing vessel for, 29, 31t

Heat fl ux calorimetric principle, 17–19, 18f, 19t

parts of, 17temperature equivalent formula

for, 17thermal contribution due to heat

capacity formula for, 17Heat fl ux microcalorimetry, DSC v.,

19–25, 20f–25fcalibration in, 23–25, 24f,

25fCalvet principle in, 22–23, 22f,

23f, 26Heat-induced transformations

oil-in-water emulsions with, 132–41, 135f, 136t, 137f, 139f, 139t, 140f







132–36, 135f, 136t

Index 381

triacylglycerols, 133whipped cream, 133

peak temperatures and heat of reaction in, 128f

protein solutions with, 119–32, 126f–128f, 130f, 131t, 132f, 141

activation enthalpy of, 129denaturation-aggregation of

globular proteins in, 124–29, 126f–128f

entropy of, 129kinetics of, 129–32, 130f, 131t,

132fLumry-Eyring model for, 129,

130f, 131tprotein structures in, 121–23thermodynamics of, 123–24,

129–32, 130f, 131t, 132fwhey protein isolate in, 125,

126f, 128f, 132fHeating mode, 35–40, 37f, 39f, 40f

aggregation, 36–37, 37fcrystallization, 36denaturation, 36–37, 37fgelatinization, 38gelation, 37–38isothermal calorimetry, 38isothermal crystallization, 39oxidative stability, 38retrogradation, 28t, 38scanning calorimetry, 35–36shelf life, 38, 39fstep heating in calorimetry, 40,

40fHeat of combustion, parameters for

food-processing design of, 225

Heat of solution, parameters for food-processing design of, 218–24, 221f–223f

Heat release rate, critical, 354–55

Heat shocking, 148, 151HHP. See High hydrostatic pressure

processingHigh hydrostatic pressure

processing (HHP), 9food-processing treatment DSC

evaluation with, 161starch gelatinization by, 341–49,

343f, 346fstarch gelatinization with, 12wheat starch suspensions by,

344–47, 346fresults with, 345–47, 346fsample preparation for, 345

High pressure calorimetry, 311–38, 313f–315f

applications of, 324–37frozen water ratio in gelatin

gels, 326–29, 328fgelatinization of starch,

330–36, 332f–334f, 335tphase stability of lipid

containing systems, 336–37, 337f

pressure shift freezing, 329–30, 329f–331f

water in pork muscle, 324–26, 326f, 327f

calibration of, 314calorimetric signal processing in,

313fcalorimetric vessels in, 313fdifferential calorimetric detector

in, 313fexperimental setup of, 314fexperimental vessel for, 315fhigh-pressure pump in, 313fhydraulic fl uid reservoir in, 313fpressure detector in, 313fpressure-transmitting fl uid with,

316schematic diagram of, 313f

382 Index

High pressure differential scanning calorimetry (HP-DSC), 51–64, 54f–56f, 60f, 62f

advantages of, 63–64applications of, 63autoclave of, 54, 54f, 55f, 57availability of, 63calibration of, 57–63, 60f, 62f

heat calibration procedure for, 60f, 61–63, 62f

necessity for, 57parameters for, 58reference substances for, 58table of corrections for, 57temperature calibration

procedure for, 58–61uncertainty with, 58

ceramic housing of, 54, 55fconstruction of, 53–57, 54f–56fdanger with, 54differential thermal analysis with,

53disadvantages of, 64fl uid medium as limit to, 52furnace of, 54, 56, 56foperating temperature range of,

56power compensation principle

with, 53setup of, 54fspindle pump of, 54, 54f

High-sensitivity calorimetry, food-processing safety with, 361

High sensitivity heat fl ux calorimeter, 26–27, 27f, 28t

batch mixing vessel for, 27, 28tfl uid mixing vessel for, 27, 28tmixing vessels for, 27, 27ftemperature control in, 26thermal conductive block of, 26

Hot spots

dryers with, 364drying and, 13

HP-DSC. See High pressure differential scanning calorimetry

HydrocolloidC80 technique v. vessel/heating



of, 19t

IC. See Isothermal calorimetryIce cream

heat capacity for, 36theat-induced transformations

with, 133ICTAC. See International

Confederation for Thermal Analysis and Calorimetry

Indium, HP-DSC calibration using, 58, 60, 61

Initial calorimetric signal θ0, calculation of, 252

International Confederation for Thermal Analysis and Calorimetry (ICTAC), 20

Interpolyelectrolyte complex formation, 107

Isoconversional methods, food-processing safety with, 360–61

Isothermal calorimetry (IC), 265heating mode with, 38shelf life analysis with, 237–61

calculation for rate constant, 254–55

calculation for reaction enthalpy, 255

calculation for reaction half-life, 254

Index 383

calculation for reaction order, 252–53

calculation for total heat released, 253–54

calculation of initial calorimetric signal θ0, 252

calculation of QT, 256–60, 260fdetermination of K, 255–56empirical model fi tting for,

246–49, 246f, 249f, 250fqualitative studies on, 239–45,

241fquantitative studies on, 245reaction kinetics based model

of, 249–51reactions that proceed to

completion for, 252–55reactions that proceed to

equilibrium for, 255–60, 260f

test for complete reaction, 255Isothermal crystallization, heating

mode with, 39Isothermal solution calorimetry, 220Isothermal titration calorimetry

(ITC), 9–10dependence of model with, 82heat of interaction measured with,

84protein in dilute solution with,

68, 77–84, 80f, 83fbinding processes in, 78, 79,

80fdata analysis for, 79–82information content, 77–78instrumentation, 78–79, 80fpower compensation design in,

78range of applicability with,

82–83, 83fshape of titration curve with,

82–83, 83f

ITC. See Isothermal titration calorimetry

Joule effect, 23–24, 24f, 25f, 32

K, determination of, 255–56KCl, salt-protein interaction with,

98, 99tKI. See Kunitz inhibitorKirchhoff’s law, 93, 96, 100Kosmotropic salts (Salting-out

salts), 95Kunitz inhibitor (KI), 94

interpolyelectrolyte complex formation’s effects on, 107

polysaccharides’ effect on protein denaturation with, 107–10, 108f

dextran sulfate with, 108fsoybean seeds with, 107

Lactobacillus plantarum, DSC analysis of, 155–58, 157f

β-lactoglobulinκ-carrageenan with, 110λ-carrageenan with, 110denaturation of, 125milk protein with, 94–95porcine, 95salt’s effect on protein

denaturation using, 96–98, 97f, 99t

Lactoseglass transition behavior of, 300fstate diagrams of, 304f

Lardcrystallization in, 190, 191f, 191tDSC and XRD with, 190–93,

191f, 192fDSC curves of, 191fSAXS of, 191t, 192fWAXS of, 191t, 192f

384 Index

Lauric acidmelting point of, 171fMICROCALIX calibration with,

177Lead, HP-DSC calibration using, 58Linoleic acid, melting point of, 171fLinolenic acid, melting point of,

171f, 172fLipids, 169. See also

Triacylglycerolsantioxidant effi cacy of, 212crystallization kinetics of, 210–11emulsifi er-water systems with,

212–14emulsions, 214melting profi le of, 209oxidative stability of, 211–12polymorphism of, 209–10, 210fquality control for, 211thermal behavior of food


Liquids, heat capacity determination for, 34–35, 34f

Listeria monocytogenesantibiotics’ effect on, 153–54,

153fDSC analysis of, 149–53, 151fheat inactivation parameters of,

159results for, 152–53, 152fsample preparations for, 149–50

Lumry-Eyring model, 104protein heat-induced

transformations with, 129, 130f, 131t

Lyophilization, DSC technique v. vessel/heating mode with, 28t

Maltodextrin (MD), moisture content of, 222–24, 223f

MASC. See Modulated adiabatic scanning calorimetry

MD. See MaltodextrinMDSC. See Modulated DSCMeat, heat capacity for, 36tMembrane mixing vessel, mixing

and reaction heat fl ux microcalorimeter with, 29–30, 31t

Methyl cellulose, denaturation temperature of 11S globulin with, 106t

MicroCal, 21MICROCALIX, 170, 176–79, 177f,

180f, 186f, 187f, 189f, 191f, 192f

cocoa butter in, 182–84, 183fexperimental setup of, 177fexperiments using, 179laboratory with conventional

x-ray source and, 177–78lauric acid for calibration of,

177synchrotron radiation XRD bench

with, 178–79temperature-controlled cryostat

for, 177ftemperature controller for, 177f

MicrocalorimetryDSC v., 16, 19–25, 20f–25f

calibration in, 23–25, 24f, 25fCalvet principle in, 22–23, 22f,

23f, 26heat fl ux, 16, 19–25, 20f–25fmethods of, 30–45, 33f, 34f, 36t,

37f–44fcontrolled relative humidity in,

45heat capacity determination,

30–35, 33f, 34f, 36theating mode in, 35–40, 37f,

39f, 40f

Index 385

mixing and reaction calorimetry, 40–43, 41f–43f

pressure calorimetry, 43–45, 44f

Milkbovine β-lactoglobulin of, 94–95DSC technique v. vessel/heating


of, 19theat capacity for, 36tisothermal traces at temperatures

for, 39fprotein, 94–95, 131skim milk powder, 222–24, 223f

Milk fatanhydrous, 133–41, 135f, 136t,

137f, 139f, 139t, 140f, 184–87, 186f, 187f

crystallization properties of, 189–90

DSC and XRD with, 184–90, 186f, 187f, 189f

globules, 188–89, 189fMixing and reaction calorimetry,

40–43, 41f–43fbatch mixing in, 40binding, 41–42dissolution, 41enzymatic reactions, 42, 42f, 43ffermentation, 43fl ow mixing in, 41neutralization, 41, 41fsolubility, 41

Mixing and reaction heat fl ux microcalorimeter, 29–30, 31t

ampoule mixing vessel for, 30, 31t

batch high-pressure vessel for, 29, 31t

batch standard vessel for, 29, 31tgas-fl ow vessel for, 29, 31t

membrane mixing vessel for, 29–30, 31t

mixing vessel for, 29, 31tMixing vessel, mixing and reaction

heat fl ux microcalorimeter with, 29, 31t

Modulated adiabatic scanning calorimetry (MASC), 265

Modulated DSC (MDSC), food-processing design with, 204

Moisture contentmaltodextrin, 222–24, 223fskim milk powder, 222–24, 223fthermodynamic response with,

219–20Moisture loss, calorimetry with, 8Molasses incident, 365Mycoplasma laidlawii, DSC

analysis of, 149Myristic acid, melting point of, 171f

NaCldenaturation temperature of 11S

globulin with, 106tsalt-protein interaction with, 98,

99tNeutralization, mixing and reaction

calorimetry with, 41, 41f(NH4)2SO4, salt-protein interaction

with, 98, 99tNMR. See Nuclear magnetic

resonanceNuclear magnetic resonance

(NMR), 301

Odorantsfood fl avorings with, 102protein denaturation effected by,

102–4, 103fBSA in, 104Lumry-Eyring model in, 104ovalbumin in, 102–4, 103f

386 Index

Oersted law, 25Oil. See also Lipids



endothermic/exothermic effects of, 19t

oxidative stability of, 211–12Oil-in-water emulsions, 132–41,

135f, 136t, 137f, 139f, 139t, 140f







132–36, 135f, 136ttriacylglycerols, 133whipped cream, 133

Oleic acid, melting point of, 171fOne-cell calorimetric principle,

18fOpen measurement method, 357,

357f, 358fOrange juice, heat capacity for,

36tOvalbumin, protein denaturation

effected by, 102–4, 103fOverlapping peaks, interpretation

of, 8Oxidative stability, heating mode

with, 38

Oxygen, reactions with, 363–64determination of self-ignition

temperature for, 364screening test for, 363

Palmitic acid, melting point of, 171f, 172f

Parameter Be, salt-protein interaction with, 98, 99t

Pectin, denaturation temperature of 11S globulin with, 106t

pHdenaturation temperature of 11S

globulin with, 106t7S globulin denaturation with

different values of, 9311S globulin denaturation with

different values of, 91f, 92protein denaturation affected by,

89–95, 91fRBPC denaturation with different

values of, 92Phaseolin, 93Phase transitions, food dehydration

in, 290, 292–93, 293fPolypeptide chains, 121Polysaccharides

denaturation temperature of 11S globulin with, 106t

protein denaturation affected by, 104–10, 106t, 108f

11S globulin in, 105–6, 106t, 110

Kunitz inhibitor in, 107–10, 108f

protein thermodynamic incompatibility with, 109–10

Porkheat capacity for, 36tmuscle

heat fl ux of crystallization for, 327f

Index 387

thawing heat fl ux of, 326fwater in, 324–26, 326f, 327f

Postdenaturation aggregationaggregation rate determined by

denaturation rate with, 111estimate of apparent denaturation

enthalpy with, 112irreversible, 110kinetic parameters of, 111of protein, 110–12reversible, 110

Potato, heat capacity for, 36tPower compensation principle, 53Pressure calorimetry, 43–45, 44fPressure shift freezing (PSF)

basic procedure of, 329fheat fl ux during, 330fhigh pressure calorimetry with,

329–30, 329f–331fice crystal/sample mass ratio

formed during, 331fpressure during, 330ftemperature during, 330f

Propylene glycol, denaturation temperature of β-lactoglobulin with, 110

Protein20 amino acids constituting, 120behavior upon heating of, 89bovine β-lactoglobulin of milk,

94–95calorimetry of dilute solution of,

67–84, 71f, 80f, 83fcold denaturation with, 75DSC data quantifi es high-

affi nity binding with, 75–77DSC for, 68–77, 71fITC for, 68, 77–84, 80f, 83f

cereal with, 12conformation stability of, 121DSC technique v. vessel/heating

mode with, 28t

emulsions/foams, role in, 10free, 106theat-induced transformations in

solutions of, 119–32, 126f–128f, 130f, 131t, 132f, 141

denaturation-aggregation of globular proteins with, 124–29, 126f–128f

kinetics of, 129–32, 130f, 131t, 132f

protein structures with, 121–23thermodynamics of, 123–24,

129–32, 130f, 131t, 132fhydrophilic component of, 291milk, 94–95, 131polysaccharides’ thermodynamic

incompatibility with, 109–10postdenaturation aggregation of,

110–12structures, 121–23

caseins, 122–23gelatin molecules, 122polypeptide chains, 121secondary structures, 121tertiary structures, 121–22

thermal analysis of cereal processing with, 272–76, 274f, 275f

gluten fi x of water molecules for, 273

soluble in aqueous media for, 273

starch gelatinization with, 274thermal behavior of food


thermal denaturation of, 87–113, 91f, 97f, 99t, 101f, 103f, 106t, 108f

effects of alcohols on, 99–100, 101f

388 Index

Protein (continued)effects of odorants on, 102–4,

103feffects of pH on, 89–95, 91feffects of polysaccharides on,

104–10, 106t, 108feffects of salts on, 95–99, 97f,

99treversibility of, 123two-state model of, 123

thermodynamic compatibility of denatured/native, 89

ultrasensitive calorimetry to, 8

Protein-protein interactions (Exothermic reaction), 127–28

PSF. See Pressure shift freezing

QT, calculation of, 256–60, 260f

Rate constant, calculation for, 254–55

RBPC. See Ribulose 1,5 biphosphate carboxylase

Reaction enthalpy, calculation for, 255

Reaction half-life, calculation for, 254

Reaction kinetics, model of based on, 249–51

Reaction order, calculation for, 252–53

RetrogradationDSC technique v. vessel/heating

mode with, 28theating mode with, 28t, 38starch, 270

Ribulose 1,5 biphosphate carboxylase (RBPC), different pH values in denaturation of, 93

Saccharides, dissolution behavior of, 219

Safety. See Food-processing safety; Transport safety

Salmon, heat capacity for, 36tSalting-in salts. See Chaotropic saltsSalting-out salts. See Kosmotropic

saltsSalts


chaotropic, 95kosmotropic, 95protein denaturation affected by,

95–99, 97f, 99tDebye-Höckel approximation

for, 98β-lactoglobulin, 96–98, 97f,

99tScanning mode, 35–36, 44. See also

Temperature-scanning modeheating curves for different rates

of, 127fScanning transitiometry, 311–38,

317f–319f, 321f, 323f, 324fapplications of, 324–37

frozen water ratio in gelatin gels, 326–29, 328f

gelatinization of starch, 330–36, 332f–334f, 335t

phase stability of lipid containing systems, 336–37, 337f

pressure shift freezing, 329–30, 329f–331f

water in pork muscle, 324–26, 326f, 327f

benzene as verifi cation test for, 322, 323f

calibration of, 322–23calorimetric vessels in, 319–20piston pump in, 320

Index 389

precautions for, 321–22pressure detector in, 320schematic diagram of, 319fscheme of basic principles of,

317ftemperature and energy scales of,

322thermodynamic scheme of, 318ftransitiometric vessels for, 321f

Self-ignition temperature, 364Setschenow equation, 100SFC. See Solid fat contentShelf life, 237–61

empirical model fi tting for analysis of, 246–49, 246f, 249f, 250f

heating mode with, 38, 39fqualitative studies on, 239–45,

241fquantitative studies on, 245reaction kinetics based model of,

249–51reactions that proceed to

completion for analysis of, 252–55

calculation for rate constant, 254–55

calculation for reaction enthalpy, 255

calculation for reaction half-life, 254

calculation for reaction order, 252–53

calculation for total heat released, 253–54

calculation of initial calorimetric signal θ0, 252

reactions that proceed to equilibrium in analysis of, 255–60, 260f

calculation of QT, 256–60, 260fdetermination of K, 255–56

test for complete reaction, 255Skim milk powder (SMP), moisture

content of, 222–24, 223fSmall-angle X-ray diffraction

(SXRD), 176lard in, 191t, 192f

SMP. See Skim milk powderSodium alginate, denaturation


Solid fat content (SFC), 209–10Solubility, mixing and reaction

calorimetry with, 41Solution calorimetry, 218Specifi c heat

moisture content’s effect on thermodynamic response with, 219–20

parameters for food-processing design of, 224–25

pharmaceutical substances, 219solution calorimetry with, 218

Spindle pump, HP-DSC, 54fSpray-drying, 290, 305–6, 306fStaphylococcus aureus

hydrostatic pressure resistance of, 44, 44f


StarchC80 technique v. vessel/heating

mode with, 31tcereal with, 12DSC technique v. vessel/heating


of, 19tgelatinization, 12, 274, 278–79,

280fcalorimetric analysis by HPP

of, 341–49, 343f, 346fheat in, 342, 343f

390 Index

Starch (continued)high pressure calorimetry on,

330–36, 332f–334f, 335tstorage of, 347–48thermodynamic data for, 335twheat, 344–47, 346f

HHP’s effects on, 12retrogradation, 270thermal analysis of cereal

processing with, 268–72, 289f–272f

aqueous suspension of starch granules for, 268

DSC for, 268–72, 289f–272fState diagrams

food dehydration in, 303–7, 304ffreeze-drying, 290, 306–7lactose, 304fspray-drying, 290, 305–6, 306f

State transitions, food dehydration in, 290, 292–93, 293f

Stearic acid, melting point of, 171f, 172f

Step heating, calorimetry with, 40, 40f

Sucrose, calorimetric curves of, 207f, 208f

SugarC80 technique v. vessel/heating

mode with, 31tcalorimetric curves of sucrose,

207f, 208fDSC technique v. vessel/heating

mode with, 28tglass transition with, 294thermal behavior of food


SXRD. See Small-angle X-ray diffraction

TA. See Thermal analysis

TCC. See Temperature-controlled cryostat

Temperature, critical, 355–56, 355fTemperature calibration procedure,

HP-DSC, 58–61Temperature-controlled cryostat

(TCC), 177fTemperature modulated DSC

(TMDSC), 265Temperature-scanning mode

blank test heat fl ow equation, 32

heat capacity determination using, 31–33, 33f

Temperature step mode, heat capacity determination using, 33–34

TG. See TriacylglycerolsTGA. See ThermogravimetryThermal analysis (TA). See also

Differential thermal analysis; Dynamical mechanical thermal analysis

design/monitor of cereal processing, 265–85

nonstarch carbohydrates, 276–78, 277f

process applications, 278–85, 280f–284f

proteins, 272–76, 274f, 275fstarch, 268–72, 289f–272f

food dehydration understood with, 298–301, 299f, 300f

food-processing design with, 203–6, 204f

methods, 205–6samples, 206techniques, 203–5, 204f

Thermogravimetry (TGA), 265Time scale, calorimetry with, 8Tin, HP-DSC calibration using, 58,

59

Index 391

TMDSC. See Temperature modulated DSC

Total heat released, calculation for, 253–54

Transitiometry scanning technique, 12

Transition state theory, 111Transport safety, 365–66Triacylglycerols (TG)

composition of, 169DSC and XRD in study of,

169–76, 171t, 172t, 173ffatty acids, 170–73, 171t, 172t,

173fcrystallographic/energetic

properties of, 172fhexagonal, 172, 172f, 173fmelting point of, 171f, 172forthorhombic perpendicular,

172, 172f, 173ftriclinic parallel, 172, 172f,

173fheat-induced transformations

with, 133main types of, 173fmelting profi le of, 209polymorphism of, 170–73, 171t,

172t, 173f, 209–10, 210fTrypsin inhibitor, 94

Vanillin, 102Van’t Hoff enthalpy change, DSC

measured, 72–74Vegetable, heat capacity for, 36t

Water. See also Oil-in-water emulsions

emulsifi er-water systems with lipids, 212–14

frozen water ratio in gelatin gels, 326–29, 328f

gluten fi x with molecules of, 273

pork muscle with, 324–26, 326f, 327f

starch-water systems, 207thermal behavior of food

constituents in, 216–17Wheat, starch gelatinization by

HHP for, 344–47, 346fresults with, 345–47, 346fsample preparation for, 345

Whey protein isolate, heat-induced transformations with, 125, 126f, 128f, 132f

Whipped cream, heat-induced transformations with, 133

Wide-angle X-ray diffraction (WXRD), 175

lard in, 191t, 192fWXRD. See Wide-angle X-ray

diffraction

Xanthan, denaturation temperature of β-lactoglobulin with, 110

X-ray diffraction (XRD), 11applications with DSC and,

179–93, 180f, 183f, 186f, 187f, 189f, 191f, 192f

cocoa butter in DSC and, 179–84, 180f, 183f

DSC with, 169–94, 171t, 172t, 173f, 177f, 180f, 183f, 186f, 187f, 189f, 191f, 192f

food-processing design with DSC and, 225

lard in DSC and, 190–93, 191f, 192f

MICROCALIX using DSC and, 170, 176–79, 177f

milk fat in DSC and, 184–90, 186f, 187f, 189f

392 Index

X-ray diffraction (XRD) (continued)results using DSC and, 179–93,

180f, 183f, 186f, 187f, 189f, 191f, 192f

triacylglycerols in DSC and, 169–76, 171t, 172t, 173f

X-ray diffraction with temperature function (XRDT), 11, 176

X-ray diffraction with time function (XRDt), 11, 176

XRD. See X-ray diffractionXRDT. See X-ray diffraction with

temperature functionXRDt. See X-ray diffraction with

time function

YeastC80 technique v. vessel/heating



of, 19tYogurt processing, DSC technique

v. vessel/heating mode with, 28t

Young’s modulus E, 265

Zinc, HP-DSC calibration using, 58

Documents

Calorimetry in Food Processing Analysis and Design of Food Systems Institute of Food Technologists Series