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Predictive Microbiology:an overview and a practical application

Møller, Cleide Oliveira de Almeida

Publication date:2013

Document VersionEarly version, also known as pre-print

Link back to DTU Orbit

Citation (APA):Møller, C. O. D. A. (Author). (2013). Predictive Microbiology: an overview and a practical application.Sound/Visual production (digital)

https://orbit.dtu.dk/en/publications/a8b19a41-9c0d-4aee-97f2-327a59b987f7

Predictive Microbiology: an overview and a practical application Cleide Oliveira de Almeida Møller

Correspondence

Cleide Møller. E-mail: [email protected]

National Food Institute, Technical University of Denmark, Søborg, Denmark.

Presenter

Presentation Notes

Good afternoon. Thanks for the introduction Søren. Today I am going to present you the work that I have developed in the past years and that resulted in the thesis with the title … This research project was carried out under supervision of Tina… and co-supervision of Paw… and Maarten… at the National Food Institute.

05/11/2013 Predictive Microbiology 2 DTU Food, Technical University of Denmark

Outline:

• Definition

• Challenges

• Applications

• Example (Eg)

Introduction

Objectives of the study

Summarizing the performed work

Process to build up the developed models

Results

Remarks and future perspectives

Presenter

Presentation Notes

At this presentation. - I am going to give you a brief introduction with an overview about the problematic involving Salmonella, ground pork and catered meals I will tell you how the objectives of this study were assembled. I will summarize the performed work I will let you know the process to build up the developed models I am also going to show you some of the results and conclude with some remarks and future perspectives


Definition:

Source: Perez-Rodrigues and Valero, 2013.

Predictive Microbiology

New area

Responses of microorganisms in foods to

environmental factors

Assurance of: food safety and

food quality

Development due to emergence of powerful

computational resources and statistics

Presenter

Presentation Notes



Challenges:

• Many different researchers have developed models for the behaviour of particular

microorganisms in laboratory media and different food matrices, and made them

available in the public domain through peer reviewed publications (Ross and

Dalgaard, 2004).

• From an industry perspective, the utility of these public-domain models is often

somewhat limited:

In many cases, models have been developed under laboratory conditions,

are based on specific combinations of parameters that might not be appropriate

for the particular food products of an industry,

and have not always been validated or even used in real food systems.

• Despite that, such models can be useful as long as their limitations are recognised

and considered in their application (Membré and Lambert, 2008).

Presenter

Presentation Notes



Applications:

Current applications of predictive microbiology in an industrial context are wide and according

to Membré and Lambert (2008) can be summarised into three groups of activities:

1. Product innovation, where new products and process are developed, existing

products are reformulated, storage conditions and shelf-life are determined, by

assessment of speed of microbial proliferation, growth limits, or inactivation rate

associated with particular food formulations and/or process conditions;

2. Operational support, where predictive models are used as support decision tools to

implement or run a food manufacturing operation, such as designing in-factory

heating regimes, setting Critical Control Points (CCPs) in Hazard Analysis and Critical

Control Points (HACCP), assessing impact of process deviations on microbiological

safety and quality of food products;

3. Incident support, where the impact on consumer safety or product quality are

estimated in case of problems with products on the market.

Presenter

Presentation Notes


Modelling transfer of Salmonella

Typhimurium DT104 during grinding of pork

C.O.A. Møller1, M.J. Nauta1, B.B. Christensen2, P. Dalgaard3, T.B. Hansen1

1 National Food Institute, Technical University of Denmark, Søborg, DK.

2 Department of Food Science, Faculty of Life Sciences, University of Copenhagen, Copenhagen, DK.

3 National Food Institute, Technical University of Denmark, Kgs. Lyngby, DK.

Correspondence

Cleide Møller. E-mail: [email protected]

Transfer study Eg

Presenter

Presentation Notes

Good afternoon. Thanks for the introduction Søren. Today I am going to present you the work that I have developed in the past years and that resulted in the thesis with the title … This research project was carried out under supervision of Tina… and co-supervision of Paw… and Maarten… at the National Food Institute.


• Salmonella is a critical pathogen (CDC, 2011; EFSA, 2010). • Pork still is an important source of salmonellosis (EFSA, 2010; van Hoek et

al., 2012; Wegener et al., 2003). • Ground meat is frequently associated with outbreaks of salmonellosis

(Stock and Stolle, 2001). • Up to 70% of foodborne illnesses are estimated to be linked to catered food

(Filion and Powell, 2011; Hensen et al., 2006; Jones et al., 2004; Lee and

Middleton, 2003). • In Denmark, 61 of 86 reported outbreaks in 2011 were associated with

outside-the-home settings (anonymous, 2012). • To model the distribution of pathogens during the processing operation are

of major relevance to risk analysts (Flores, 2006).

Introduction Transfer study Eg

Presenter

Presentation Notes

This project was carried out because 1) Salmonella is one of the most important agents causing foodborne diseases worldwide. 2) Despite the intervention strategies in the primary sector, pork still contributes significantly to the public health disease burden cause by Salmonella infections in European countries. 3) And ground meat is one of the food items rather frequently associated with outbreaks of salmonellosis. 4) The WHO estimates that up to 30% of individuals in developed countries become ill from contaminated food or water each year, and up to 70% of these illnesses are estimated to be linked to foods service facilities. 5) In Denmark, the majority of reported outbreaks in 2010 were associated with outside-the-home settings such as restaurants, canteens, hotels, schools, shops, institutions and sport events 6) and there are no doubts about the relevance of studies that simulate and model the distribution of pathogens during the food processing, specially to risk analysis in order to ascertain the importance of equipment sanitation, sources of potential product contamination, and improved equipment design.


The aim of this study was to develop a model able

to predict cross contamination of Salmonella in

pork grinding.

Objective

Transfer study Eg


Experimental work Transfer study Eg

S. Typhimurium

DT104

37°C ,overnight + 5°C, 1-3 days

Ca.109 Salmonella/g

Transfer Modelling

Salmonella

Up to100 slices

Matrix

Distribution of Salmonella

- Analysis performed

- Build up the model

- Validation

37°C, 24h

22°C, 3d

5 4 3 2 1

5 4 3 2 1

A C E G I

B D F H J


Describing the transfer rates of Salmonella during pork grinding

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

10.00

0 10 20 30 40 50 60 70 80 90 100

Salm

onel

la c

ount

s (lo

g C

FU/p

ortio

n)

Portion number

observed data

Transfer study Eg


Modelling cross-contamination during pork grinding

a

bc2

c1

meatmachine

a: transfer of Salmonella from met to grinderb: transfer of Salmonella from grinder to meatc1: Salmonella inactivated on grinder during the processc2: Salmonella inactivated on the ground meat

Salmonellainput

Salmonellaoutput

Grinder surfaces

Nauta et al. (2005) Model

Transfer study Eg



Transfer rates of Salmonella DT104 based on cell count data fitted to the suggested model

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

10.00

0 10 20 30 40 50 60 70 80 90 100

Salm

onel

la c

ount

s (lo

g C

FU/p

ortio

n)

Portion number

observed data

Nauta et al. (2005) model

Transfer study Eg


Modelling cross-contamination during pork grinding

Suggested Model

a 1 - transfer from meat slice to E1

a 2 - transfer from meat slice to E2

b 1 - transfer from E1 to ground meat

b 2 - transfer from E2 to ground meat

c 1 - inactivation in E1

c 2 - inactivation in E2

c 3 - inactivation in meat

- grinder surfaces

- grinding process

Meat c 2

c 1

c 3

a 1

a 2

b 1

b 2

Salmonella input

Salmonella output

Environment 1 (E1)

Environment 2 (E2)

Environment 1 (E1)

Environment 2 (E2)

Grinder surfaces

Transfer study Eg



Transfer rates of Salmonella DT104 based on cell count data fitted to the suggested model

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

10.00

0 10 20 30 40 50 60 70 80 90 100

Salm

onel

la c

ount

s (lo

g C

FU/p

ortio

n)

Portion number

observed data

Nauta et al. (2005) model

suggested model

Transfer study Eg


Challenges in cross-contamination during pork grinding

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

0 10 20 30 40 50 60 70 80 90 100 110 120

Salm

onel

la c

ount

s (lo

g C

FU/p

ortio

n)

Portion number

data from challenge test

parameters from dataset 3Suggested model

Transfer study Eg

Presenter

Presentation Notes

We performed some challenge tests in order to validate our suggested model. And as possible to observe at this figure our model could efficiently describe the observed data obtained after to process 110 slices, even with the insertion of contaminated pieces of meat at different moments, not only at the beginning of the process but also at different occasions through the process. This good performance of the model is also observed when different levels of Salmonella are tested. Bias and accuracy factors values obtained using the parameter estimates from the three different datasets indicated good performance in all cases.


• Tail phenomenon

Food processors

o Control measures

o Cleaning and sanitization

• Observed transfer successfully modelled

• Model can describe different processes

• Tool to support risk assessors

Remarks and future perspectives

• To be investigated: Food matrices Pathogens Inoculum levels Processings


Available pathogen modeling programs

http://ucfoodsafety.ucdavis.edu/Food_Safety_Links/Pathogen_Modeling_Programs/#

Model name URL Applicability 1) American Meat

Institute Process Lethality Determination Spreadsheet

http://ucfoodsafety.ucdavis.edu/files/40348.pdf

effectiveness of a specific heat process to destroy microorganisms of concern.

2) ComBase Predictor http://www.combase.cc/index.php/en/predictive-models/134-combase-predictor

based on observations made in culture media, and comprise a set of 20 growth models, seven thermal death models and two non thermal survival models. Temperature, pH and aw (usually as a function of NaCl) are the core factors

http://ucfoodsafety.ucdavis.edu/Food_Safety_Links/Pathogen_Modeling_Programs/



http://www.combase.cc/index.php/en/predictive-models/134-combase-predictor

http://www.combase.cc/index.php/en/predictive-models/134-combase-predictor




Model name URL Applicability 3) DMRI - predictive

models for meat

http://dmripredict.dk/Default.aspx?ReturnUrl=%2fModels%2fMeatSafety%2fDefault.aspx

Can be used to predict aspects of food safety and also food quality. All of the prediction tools from DMRI are based on data obtained from experiments with meat products.

4) Isothermal-Based Prediction Tool, IBPT

http://www.meathaccp.wisc.edu/pathogen_modeling/therm.html can predict whether Salmonella, E. coli O157:H7, or S. aureus will grow to a “level of concern” in raw beef and pork products.


http://dmripredict.dk/Default.aspx?ReturnUrl=/Models/MeatSafety/Default.aspx

http://dmripredict.dk/Default.aspx?ReturnUrl=/Models/MeatSafety/Default.aspx

http://www.meathaccp.wisc.edu/pathogen_modeling/therm.html




Model name URL Applicability 5) OptiForm Listeria

Control Model 2007 http://www.purac.com/EN/Food/Brands/OptiForm.aspx help to calculate the level

of lactate and diacetate needed to control Listeria in cured and uncured cooked meat and poultry products for their required shelf life.

6) Risk Management Tool for the Control of Campylobacter and Salmonella in Chicken Meat (Version 1.0)

http://www.mramodels.org/poultryRMTool/

To be used in conjunction with the Codex Guidelines for the Control of Campylobacter and Salmonella in chicken meat.


http://www.purac.com/EN/Food/Brands/OptiForm.aspx

http://www.mramodels.org/poultryRMTool/




Model name URL Applicability 7) Seafood Safety and

Spoilage Predictor, SSSP v 3.0

http://sssp.dtuaqua.dk/ To predict the simultaneous growth of Listeria monocytogenes and lactic acid bacteria in lightly preserved seafood.

8) USDA Pathogen Modeling Program

http://ars.usda.gov/Services/docs.htm?docid=11550 Estimates the effects of multiple variables on the growth, inactivation or survival of foodborne pathogens. Most of the models are based on experimental data of microbial behavior in liquid microbiological media.


http://sssp.dtuaqua.dk/

http://ars.usda.gov/Services/docs.htm?docid=11550


This research project was financed by the

Technical University of Denmark through the

FoodDTU programme

Constructive advice and critical comments

were given by:

Acknowledgements

Tina Beck Hansen

Paw Dalgaard

Maarten Nauta

Bjarke Bak Christensen

Skillful technical assistance was provided by colleagues

from the Division of Food Microbiology at the National Food

Institute.

Diana Karapetian

Rikke Krag

Grethe Fisher

Mette Kemp

Kate Vibefeldt

Tina Birk

Jens Kirk

Sidsel Henriksen

Louise Vigsnæs

Birthe Hald

Naseer Shukri

Anne Mette Bollerslev

Lars Bogø Jensen

Lasse Ørum-Smidt

Tina Beck Hansen

Søren Aabo

Annette Nygaard Jensen

Cleide Møller


Acknowledgements

Documents

Predictive Microbiology: an overview and a practical application · microorganisms in laboratory media and different food matrices, and made them available in the public domain through