Senso Metrics Program

SENSOMETRICS 2008EXHIBITORS

SENSOMETRICS 2008

Discover a New World of Data1

The Organizing Committee of the 9th Sensometrics Conference is pleased to welcome you to Brock University.

Located at the centre of Canadas beautiful Niagara Peninsula in St. Catharines, Ontario, Brock University is the only Canadian university with the distinction of being part of a UNESCO Biosphere Reserve.This region of Niagara presents the visitor with a world of opportunities. Niagara is of course famous for the largest waterfall in the world. Although the Niagara River is only 50 km between two Great Lakes, Erie and Ontario, it is one of the most spectacular rivers you can easily see. The microclimate caused by the Niagara Escarpment and Lake Ontario has provided a fertile home for over 80 vineyards of Eastern Canadas largest wine region. The region has been a tourist destination since the 1800s and provides a wide range of activities for families, sports enthusiasts and culture seekers. Ontarios population comes from around the world and is extremely welcoming to international visitors. During the social part of our program, we hope to expose you to some of the culinary features of this part of Canada. Brock University is located within 110 km of Toronto, Canadas largest city. If you have the time its wonderful to explore the most multicultural city in the world.

For those of you who are here strictly for the conference, we hope you will enjoy the scientific program. The depth and variety of subjects should provoke interesting discussions and perhaps future directions for the field of Sensometrics.

If there is any way in which the Organizing Committee can make your visit more beneficial, please contact any of us. We hope that this conference will live up to the tradition of previous Sensometrics.

Chris Findlay & Isabelle LesschaeveCo-Chairs of the 9th Sensometrics Conference

ORGANIZING COMMITTEEB. Thomas Carr, USA John Castura, Canada Dana Craig-Petsinger, USA Kernon Gibes, USA Jean-Franois Meullenet, USA Todd Renn, USA

ORGANIZERS GREETINGS

SENSOMETRICS 2008


Dear Sensometrics 2008 participant,

On behalf of the Sensometrics Society it is a pleasure for me to welcome you to this 9th edition of the Sensometrics conferences, now for the third time on the North American continent:

1992 Leiden, Holland, 2000 Columbia, MS, USA,1994 Edinburgh, Scotland, 2002 Dortmund, Germany,1996 Nantes, France, 2004 Davis, CA, USA,1998 Copenhagen, Denmark, 2006 s, Norway.

In 1992, the conference started as a small workshop, but has now developed into the main forum for the data analytical and modelling end of the sensory and consumer science area. Since the Sensometrics Society foundation in the year 2000, the conferences have been organized within the framework of the society in close connection with Elsevier and Food Quality and Preference.

The Sensometric Society Aims are to increasetheawarenessofthefactthatthefieldofsensoryandconsumer science needs it own special methodology and statistical methods; improvethecommunicationandco-operationbetweenpersonsinterestedin the scientific principles, methods and applications of sensometrics; actastheinterdisciplinaryinstitution,worldwide,todisseminatescientific knowledge on the field of sensometrics

Our Canadian friends and organizers have made a programme for us this year that fully meets these aims and I wish you all a stimulating conference.

Per Bruun BrockhoffChairman of the Sensometrics Society

SENSOMETRICS SOCIETY GREETINGS

ChairmanPer Bruun BrockoffProfessor in StatisticsInformatics and Mathematical ModellingRichard Petersens Plads, Building 321, Room 032Technical University of DenmarkDK-2800 Kongens Lyngby, Denmark

[email protected] +45 45 25 33 65

PROGRAM

Sunday, July 20, 2008

REGISTRATION AT BROCK UNIVERSITY, EARP RESIDENCE

13:00 - 17:00

WELCOME RECEPTION AND BARBEQUE AT BROCK UNIVERSITYPOND INLET

18:00 - 21:00

Monday, July 21, 2008

REGISTRATION AT BROCK UNIVERSITY, ACADEMIC SOUTH (AS) LOBBY 8:00 - 9:00

CONFERENCE OPENINGChris Findlay and Isabelle LesschaeveWelcome to Brock UniversityDr. Gregory Finn Vice Provost - Brock University

8:45 - 9:00

PLENARY SESSION ONENew ways to describe, compare, evaluate, and analyze products and assessors Herv Abdi, The University of Texas at Dallas, AS 203

9:00 - 10:00

NUTRITION BREAK, AS HALLWAY 10:00 - 10:30

TECHNICAL SESSION A: New MethodsSession Chair: B. Thomas Carr, AS 203

10:30 - 12:00

A-1 The application of check-all-that-apply consumer profiling to preference mapping of vanilla ice cream and its comparison to classical external preference mappingJean-Francois Meullenet, Youngseung Lee, Lauren DooleyUniversity of Arkansas, Fayetteville, AR, United States

A-2 Polarized Sensory Positioning: A new sensory method based on similarity scaling to product referencesEric Teillet1, Pascal Schlich2, Philippe Courcoux3, Christine Urbano21Lyonnaise des Eaux, Dijon, France, 2Centre Europen des Sciences du got, CNRS-UB-INRA, Dijon, France, 3ENITIAA, Unit de Sensomtrie et Chimiomtrie, Nantes, France

A-3 Ratios of Normal Variables in Superiority ClaimsJohn Ennis, Daniel EnnisThe Institute for Perception, Richmond, VA, United States

A-4 Consumer Driver studies using Bayesian NetworksFabien Craignou1, Lionel Jouffe21Repres, Paris, France, 2Bayesia, Laval, France

A-5 Consumer Segmentation of BIB liking data of 12 Cabernet Sauvignon wines: A case study.Chris FindlayCompusense Inc, Guelph, Ontario, Canada

SENSOMETRICS 2008


July 20 23, 2008, Brock University,St. Catharines, Ontario, Canada

LUNCH DINING ROOM, POSTERS EXHIBITS 12:00 - 13:00

TECHNICAL SESSION B: Descriptive Analysis DataSession Chair: Kernon Gibes, AS 203

13:00 - 15:30

B-1 Panel Concordance Analysis (PANCA)Eduard DerksDSM Food Specialties, Delft, Netherlands

B-2 Using Range Voting Analysis Techniques for Odor Profiling DataGregory Keep, William RaynorKimberly-Clark Corp., Neenah, Wisconsin, United States

B-3 Napping by modality: a happy medium between analytic and holistic approachesJohann C. Pfeiffer, Chantal C. GilbertCampden & Chorleywood Food Research Association, Chipping Campden, United Kingdom

B-4 Accounting for scaling differences in sensory profile dataPer Bruun Brockhoff, Niels Axel SommerTechnical University, Denmark, Lyngbyd, Denmark

B-5 Automated and visual mixed model ANOVA of sensory profile dataNiels Axel Sommer, Per Bruun BrockhoffTechnical University, Denmark, Lyngbyd, Denmark


WORKSHOP A: Consumer Descriptive Analysis: Myth or Reality?Pieter Punter and Sbastien L, AS 203

16:00 - 18:00

IntroductionPieter H. PunterOP&P Product Research, Utrecht, Netherlands

WA-1 How reliable are the consumers?Comparison of sensory profiles from consumers and expertsThierry Worch1, Sbastien L2, Pieter H. Punter11OP&P Product Research, Utrecht, Netherlands, 2Agrocampus Rennes, Rennes, France

WA-2 Comparing sensory profiles derived from consumer and trained panelsEhrhard Koehn1, Dirk Minkner21Hamburg University of Applied Sciences, Hamburg, Germany, 2BAT Germany, Hamburg, Germany

WA-3 Statistical treatment of Flash profile on consumers dataMarc DanzartAgroParisTech, Paris, France

WA-4 Methodology for analysing sensory descriptions provided by consumersJrme Pags, Julie Josse, Franois HussonAgrocampus Rennes, Rennes, France,

BUSES TO CHTEAU DES CHARMES WINERY TOUR AND DINNER 19:00

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Tuesday, July 22, 2008

SENSOMETRICS SOCIETY ANNUAL MEETING, AS 202 8:00 - 9:00

PLENARY SESSION TWOEstimating Individual Preferences with Flexible Discrete Choice ModelsJuan de Dios Ortzar, Pontificia Universidad Catlica de Chile, AS 203

9:00 - 10:00


TECHNICAL SESSION C: Linking data sets - CorrelationsSession Chair: Jean-Franois Meullenet, AS 203

10:00 - 10:30

C-1 Discrimination tests with sureness judgements: Comparison of Thurstonian and R-Index analysisGraham CleaverUnilever R&D, Vlaardingen, Netherlands

C-2 A Variation on External Preference MappingDave PlaehnInsights Now, Inc., Corvallis, OR, United States

C-3 Comparison of three methodologies to identify drivers of liking of milk dessertsGastn Ares, Ana Gimnez, Cecilia Barreiro, Adriana GmbaroSeccin Evaluacin Sensorial. Facultad de Qumica. Universidad de la Repblica, Montevideo, Uruguay

C-4 Two-step procedure for classifying consumers in a L-structured data contextIsabella Endrizzi1, Flavia Gasperi1, Evelyne Vigneau21IASMA Research Centre, Agrifood Quality Department, Via E. Mach,1, 38010 S. Michele (TN), Italy, 2ENITIAA/INRA ,Unit de Sensomtrie et de Chimiomtrie, la Graudire BP 82225 Nantes Cedex, France

C-5 Analysing four-way sensory data resulting from individual vocabulary profiling: A com-parison between HMFA and PARAFAC2Gatan LorhoNokia Corporation, Helsinki, Finland

LUNCH DINING ROOM, POSTERS EXHIBITS 12:00 - 13:00

TECHNICAL SESSION D: Qualitative DataSession Chair: Todd Renn, AS 203

13:00 - 15:30

D-1 A procedure for statistical treatment of free sorting dataEl Mostafa Qannari1, Veronique Cariou1, Eric Teillet2, Pascal Schlich31ENITIAA/INRA, Nantes, France, 2Lyonnaise des Eaux, Dijon, France, 3INRA/CESG, Dijon, France

D-2 Free listing: a method to gain initial insight of a food category.Guillermo Hough, Daniela FerrarisInstituto Superior Experimental de Tecnologa Alimentaria, Nueve de Julio, Argentina

SENSOMETRICS 2008

Discover a New World of Data

PROGRAM

5


D-3 A novel Factorial Approach for analysing Sorting Task dataMarine Cadoret, Sbastien L, Jrme PagsAgrocampus, Rennes, France

D-4 An original use of Pearsons correlation to construct a unique assessment procedure from individual ones for dynamic hedonic tests of carsCeline Astruc1, David Blumenthal1, Julien Delarue2, Marc Danzart2, Jean-Marc Sieffermann21Renault, Guyancourt, France, 2Agroparistech, Massy, France

D-5 Mixed Logit Modelling of Paired preference dataMark WohlersHortresearch, Auckland, New Zealand


WORKSHOP B: Panel CheckingPer Lea & Sbastien L, AS 203

16:00 - 18:00

IntroductionPer LeaMatforsk AS - Nofima Food, s, Norway

WB-1 Measuring discrimination in sensory panel dataChris CrockerMMR Research Worldwide Ltd, Wallingford, Oxfordshire, UK

WB-2 Checking panel performance: Why & HowPer LeaMatforsk AS - Nofima Food, s, Norway

WB-3 Panelist monitoring and trackingThierry Worch1, Raymond Delcher21OP&P Product Research, Utrecht, Netherlands2former student, ENSAR/AgroCampus Rennes

WB-4 Quali-SenseDongSheng BuCAMO Software Inc, Woodbridge, NJ

WB-5 Panel Performance with SensobasePascal SchlichCentre Europen des Sciences du Got, Dijon, France

WB-6 Demonstration of SensoMineR and panel performance functionsSbastien L & Franois HussonAgrocampus Ouest, Rennes, France

SENSOMETRICS CONFERENCE DINNER, POND INLET 19:00

6

Wednesday, July 23, 2008

REGISTRATION AT BROCK UNIVERSITY, AS LOBBY 7:30 - 8:30

MINI-SYMPOSIUM: EquivalencyChair: John Castura, AS 203

8:30 - 10:00

S-1 IntroductionHypothesis testing for equivalence defined on symmetric open intervalsDaniel M. EnnisThe Institute for Perception, Richmond, Virginia, United States

E-2 Comments & ReviewPer Bruun BrockhoffTechnical University of Denmark, Denmark

E-3 Comments & ReviewMichael MeynersNestl Research Center, Switzerland

E-4 Comments & ReviewHarry T. LawlessCornell University, New York, United States


PARALLEL: TECHNICAL SESSION E: PotpourriSession Chair: Isabelle Lesschaeve, AS 203

10:30 - 12:00

E-1 Influence of visual masking technique on the assessment of two red wines by trained and consumer assessorsCarolyn Ross1, Jeffri Bohlscheid2, Karen Weller11Washington State University, Pullman WA, United States, 2University of Idaho, Moscow ID, United States

E-2 The application of Thurstonian models for replicated difference testsRune H.B. Christensen, Per B. BrockhoffTechnical University Denmark, Lyngby, Denmark

E-3 Influence of experimental design in paired comparison studies:How to reduce duration of the experiments?Emmanuelle Diaz1, Michel Semenou2, Philippe Courcoux2, Pauline Faye11PSA Peugeot Citron, Vlizy Villacoublay, France, 2ENITIAA, Laboratoire de Sensimtrie et de Chimiomtrie, Nantes, France

E-4 Recovery of Subsampled Dimensions by Multifactor Analysis of Projective Mapping and Multidimensional Scaling of Sorting DataHarry Lawless, Michael NestrudCornell University, Ithaca, NY, United States

E-5 A Thurstonian Model for the Unspecified Hexad TestKeith Eberhardt, Victoire Aubry, Karen RobinsonKraft Foods, East Hanover, NJ, United States

SENSOMETRICS 2008


PROGRAM

7

PARALLEL: WORKSHOP CCommunicating Statistics to Non-Technical PeopleAnne Hasted, Chantal Gilbert & Chris Findlay, AS 202

10:30 - 12:00

CONFERENCE CLOSING, AS203Announcement of Sensometrics 10

12:00

BOX LUNCH 12:15

SCIENTIFIC COMMITTEE Chris Crocker, UK John Castura, CanadaDaniel M. Ennis, USA Michael Meyners, GermanyFrank Rossi, USA Mostafa Qannari, FranceGuillermo Hough, Argentina Pascal Schlich, FranceHal MacFie, UK Per Bruun Brockhoff, DenmarkIan Wakeling, UK Pieter Punter, NetherlandsJean McEwan, UK Richard Popper, USA Jean-Franois Meullenet, USA Sara Jaeger, New ZealandJrme Pags, France


8

P-1 Study of the sensory profile of chocolate-flavoured beverage made from soyAnglica Aparecida Maurcio, Paula Bucharles Barbosa, Franciane Colares Souza, Ana Paula M Buainain, Maria Ivone M J Barbosa, Helena Maria A Bolini, Roberto Marclio, Fernado T Celis, Ariane Vendemiatti, Glaucia A Rocha, Mari M Tomikawa, Michely Copa-biango, Pollyanna I SilvaState University of Campinas, Campinas, So Paulo, Brazil

P-2 Influence of packaging on the acceptability of different beer commercial brandsPaula Bucharles Barbosa, Milene M Ribeiro, Hilton L Galvo, Valria P R Minim, Suzana Della LuciaFederal University of Viosa, Viosa, Minas Gerais, Brazil

P-3 Comparison of Projective Mapping and Descriptive Analysis Using Milk and Dark Choco-latesJessica Kennedy, Hildegarde HeymannUniversity of California, Davis, Davis, CA, United States

P-4 Measuring discrimination in sensory panel dataChris CrockerMMR Research Worldwide Ltd, Wallingford, Oxfordshire, United Kingdom

P-5 Using Generalized Procrustes Analysis to capture individual differences in perceived complexity for ice tea beveragesClaire Boucon, Cline Petit, Chantalle Groeneschild, Garmt Dijksterhuis, Graham CleaverUnilever Food and Health Research Institute, Vlaardingen, Netherlands

P-6 Performance and Representation of Euclidian Distance Ideal Point Mapping (EDIPM) using a Third DimensionJean-Francois Meullenet, Joshua Tubbs, Gaewalin OupadissakoonUniversity of Arkansas, Fayetteville, AR, United States

P-7 Evaluation of the sensories attributes mango flavor, sweetness and sourness of traditional and light mongo nectars (Mangifera indica L) by Time-Intensity methodologyValria Maria Caselato de Sousa, Paula Bucharles Barbosa, Anglica Aparecida Maurcio, Franciane Colares Souza, Katia M V. A. Bittencourt Cipolli, Helena Maria Andr Bolini, Claudete S. Jimenez, Clvia D. P. C. Castro, Daniel Gomes, Giselle de A. Rodrigues, Thais F. P. de A. FreitasState University of Campinas, Campinas, Brazil

P-8 Why Additivity?William RaynorKimberly-Clark Corp., Neenah, Wisconsin, United States

P-9 Counting your Losses: Applications of Poisson RegressionRichard ZeppKimberly-Clark Corp, Neenah, Wisconsin, United States

P-10 Monte Carlo based test for the unidimensionality of a Brazilian coffee sensory panelIsabel Amorim, Eric Ferreira, Renato Lima, Rosimary PereiraFederal University of Lavras, Lavras, Minas Gerais, Brazil

P-11 Bootstrap inference in Generalized Procrustes analysis for evaluating samba schools in Brazilian carnivalEric Ferreira, Daniel FerreiraFederal University of Lavras, Lavras, Minas Gerais, Brazil

SENSOMETRICS 2008


POSTERS

9

P-12 SensR: An R-package for sensory discrimination dataRune H.B. Christensen, Per B. BrockhoffTechnical University, Denmark, Lyngby, Denmark

P-13 A statistical model for A-Not A data with and without surenessRune H.B. Christensen1, Graham Cleaver2, Per B. Brockhoff11Technical University, Denmark, Lyngby, Denmark, 2Unilever Research, Vlaardingen, Netherlands

P-14 A multidimensional scaling approach to multivariate paired comparison data for sensory scienceStephen Bennett, Trevor CoxUnilever Research and Development, Wirral, Merseyside, United Kingdom

P-15 Drivers of liking for grape nectars in the traditional commercial and light versions using Partial Least Squares RegressionLeonardo Rangel Alves, Helena Maria Andr BoliniPERCEPTION, Campinas, Brazi

P-16 Gold Kiwifruit Leather Product Development using Quality Function Deployment approachSuteera Vatthanakul1, Anuvat Jangchud1, Kamolwan Jangchud1, Nantawan TerdThai1, Brian Wilkinson21Department of Product Development, Kasetsart University, Bangkok, Thailand, 2Institute of Food Nutrition and Human Health, Massey University, Palmerston North, New Zealand

P-17 Development of a new questionnaire assessing the different facets of wine consumers involvementAnnie Rossi, Isabelle LesschaeveBrock University - CCOVI, St. Catharines, Ontario, Canada

P-18 Revisiting preference in external preference mappingMarc Danzart, Julien Delarue, Jean-Marc SieffermannAgroParisTech, Massy, France

P-19 Multidimensional unfolding models for preference dataPhilippe Courcoux, Michel SmnouENITIAA, Nantes, France

P-20 Consumer Preferences for Visually Presented MealsGorm Gabrielsen1, Margit Dall Aaslyng2, Eli Vibeke Olsen2, Hans Henrik Reisfelt3, Maria Bjerre3, Per Mller31Copenhagen Business School, Copenhagen, Denmark, 2Danish Meat Research Institute, Roskilde, Denmark, 3Department of Food Science, University of Copenhagen, Copenhagen, Denmark

P-21 The distribution of the Rv statistic for comparing multivariate configurationsMichael Nestrud, Harry LawlessCornell University Department of Food Science, Ithaca, NY, United States

P-22 Generalized Procrustes Bootstrap regions for distinguishing skin cosmetic for marketEric Ferreira, Luana FilFederal University of Lavras, Lavras, Minas Gerais, Brazil, 2O Boticrio, Curitiba, Paran, Brazil

SENSOMETRICS 2008


POSTERS

10

P-23 Exploring leads to make preference mapping more operational in an automotive contextDavid BlumenthalRenault, Guyancourt, France

P-24 Estimating variability in global inference generationLynne J. Williams1, Joseph P. Dunlop21University of Western Ontario, London, Ontario, Canada2University of Texas at Dallas, Richardson, Texas, United States

P-25 Sensory profile of a new ready-to-drink passion fruit juice beverage with different sweetener systems and its changes during storage at room temperature and under refrigerationRenata De Marchi1, Mina M.R. McDaniel2, Nilda D.M. Villanueva3, Mercedes A. Valdivia3, Helena M.A. Bolini11Universidade Estadual de Campinas, Campinas/Sao Paulo, Brazil,2Oregon State University, Corvallis/Oregon, United States, 3Pontificia Universidad Catlica del Per, Lima/Lima, Peru

P-26 Design of a new category of spirit: Pisco with rum flavor profileGerard Casaubon1, Eduardo Agosin4, Chris Findlay2, Patricio Azocar3, Pedro Bastias31Centro de Aromas, Universidad Catolica de Chile, Chile, 2Compusense Inc., Canada, 3Capel, Chile, 4Department of Chemical and Bioprocess Engineering, Universidad Catlica de Chile, Chile

P-27 Using Sensory Profiling and Multivariate Analysis in The Replication of a Competitors ProductDongsheng Bu1, Marion Cuny2, Ingrid Mage2, Martin Kermit2 and Valerie Lengard21Camo Software Inc., One Woodbridge Center, Suite 319, Woodbridge, NJ 07095 2Camo Software AS, Nedre Vollgate 8, N-0158 Oslo, Norway

SENSOMETRICS 2008


POSTERS

11

SENSOMETRICS 2008


TECHNICAL SESSION A

New Methods

B. Thomas Carr, Session Chair

13TECHNICAL SESSION ANew Methods

A-1

The application of check-all-that-apply consumer profiling to preference mappingof vanilla ice cream and its comparison to classical external preference mapping

Jean-Francois Meullenet, Youngseung Lee, Lauren Dooley

University of Arkansas, Fayetteville, Arkansas, United States

Check-all-that-apply questions regarding consumer products perceived attributes have beenused in consumer studies to determine what sensory attributes may be characteristic of aspecific product. This type of methodology has the advantage of gathering information onperceived product attributes without requiring scaling. Some researchers already advocate theuse of consumer sensory profiling to lead product development as a replacement to classicalsensory profiling. This study proposes the use of check-all-that-apply attribute responses as analternative to consumer attribute intensity ratings. The objectives of this study were (1) toevaluate the use of check-all-that-apply data for the creation of preference maps and (2) tocompare these maps to classical external maps generated from traditional sensory profiles.

Ten commercial vanilla ice cream products retailed in the United States were presented to aconsumer panel of 80 regular consumers over a period of two days according to a randomizeddesign balanced for presentation order. Consumers answered an overall liking question usingthe 9-point verbal hedonic scale as well as a check-all-that-apply question with 13 attributesthought to describe the sensory attributes of vanilla ice cream. The same products with 23attributes were also profiled by a trained descriptive panel of 17 individuals in two replications.

The counts for each of the 13 attributes in the check-all-that-apply question were compared tothe descriptive profiles via Multiple Factor Analysis (MFA), using FactoMineR in R 2008,v.2.6.2. The data was then submitted to three types of preference mapping. Externalpreference mapping (Danzart, 2004) using the descriptive profiles, or the attribute counts, wereperformed and the group ideal point determined. An internal map was also constructed followingEuclidian Distance Ideal Point Modelling (Meullenet et al., 2007). The commercial products andoptimal point configurations in these three maps were analyzed for similarity by MFA.

The MFA comparing the characterization of the products by descriptive analysis and check-all-that-apply counts showed very good agreement between the methods although only 51% of thevariability was explained by the first 2 MFA dimensions. The variable correlation circle showed astrong correlation between descriptive (d) mouthcoat, smoothness and melting, and consumer(c) perceived creaminess, between degree of ice (d) and icy (c), elasticity (d) and gummy (c),and between caramelized (d) and corn syrup (c). The average individual consumer fit wassimilar for the descriptive panel map (R

2=0.59) to that for the check-all-that-apply map

(R2=0.61). MFA of map configurations showed fair agreement between the techniques used to

produce the three maps. The first 2 MFA dimensions explained 81% of the data variability. Inparticular, the optimal product showed little variation between the methods.

Overall, we conclude that check-all-that-apply attribute data applied to preference mappinggave similar results to external preference mapping. The advantage of this technique is that thetask asked of consumers is simple (i.e., when compared to intensity ratings), and that theresponses may be more spontaneous than when intensities are rated. The limitation of thisapproach is that the optimal profile derived from the check-all-that-apply maps is in terms ofresponse counts and not intensities as given by a trained panel.

A-2

Polarized Sensory Positioning :A new sensory method based on similarity scaling to product references

Eric Teillet1, Pascal Schlich

2, Philippe Courcoux

3, Christine Urbano

2

1Lyonnaise des Eaux, Dijon, France,

2Centre Europen des Sciences du got, CNRS-UB-INRA,

Dijon, France, 3ENITIAA, Unit de Sensomtrie et Chimiomtrie, Nantes, France

In a project aiming at understanding consumer preferences and behaviours towards drinkingwater, several sensory methodologies have been applied to describe the taste of waters. Sincewater is supposed to have almost no taste, it was quite challenging to validate a list of sensoryattributes able to discriminate among waters. However, sorting tasks provided actual productdiscrimination, but are difficult to use with a small number of products and do not allowaggregating data from several studies. Nevertheless, these usual techniques agreed on theexistence of three different types of water taste linked to the overall level of mineralisation of theproducts. The basic idea of our new technique was thus to compare each product of a study to3 mineral waters from the market (the poles) each being a prototype of each of these 3 tastes.Polarized Sensory Positioning (PSP) asks the panellists to give similarity scores between theproduct from the study and each of the 3 poles. As the taste of the sensory poles is stable overtime, PSP enables aggregating data from several studies.

The continuous dissimilarity scale is anchored Exactly the same taste on the left andCompletely different taste on the right. These continuous data result in individual product*polematrices which can be analysed either with classical statistical techniques such as PCA orSTATIS (assuming poles are attributes) or with multidimensional unfolding methods taking intoaccount that these rectangular matrices contain dissimilarities between two sets of objects(products from the study and poles). Thus, unfolding is theoretically better suited to the type ofdata collected by PSP. Besides internal preference mapping, this is to our knowledge the onlyapplication of unfolding techniques in sensory analysis.

In our study, 32 panellists evaluated 10 waters by PSP. Both classical and unfolding techniquesgave the same pattern of results. However, other criteria (such as discrimination power andinterpretation) were used to compare the techniques and turned out to the advantage of theunfolding technique.

All in all, PSP is a technique which is somewhere in between profiling and sorting tasks. It isapplicable when stable over time product references can be selected to span the sensory mapunder study. Its main advantages are no need of panellist training and possibility of dataaggregation from several studies. Of course, PSP could be used with other type of productsthan water.

142008 Sensometrics Meeting



A-3

Ratios of Normal Variables in Superiority Claims

John Ennis, Daniel Ennis

The Institute for Perception, Richmond, Virginia, United States

Some applications of ratios of normal random variables require both the numerator anddenominator of the ratio to be positive if the ratio is to have a meaningful interpretation. In theseapplications, there may also be some likelihood that the variables will assume negative values.An example of such an application is when efficacy comparisons are made to support astatement such as Product X reduces malodor twice as much as Product Y. In theseapplications, treatments may have either efficacious or deleterious effects on different trials.Classical theory on ratios of normal variables has been focused on the distribution of the ratioand has not formally incorporated this practical consideration. When this issue has arisen,approximations have been used to address it. In this talk we will present an exact method fordetermining confidence bounds for ratios of normal variables using a conditional method anddemonstrate that Fiellers classical result is a special case. Several practical applications of thismethod will be illustrated.

A-4

Consumer Driver studies using Bayesian Networks

Fabien Craignou1, Lionel Jouffe

2

1Repres, Paris, France,

2Bayesia, Laval, France

Bayesian Networks are a powerful tool to model probabilistic relations between variables.Widely used in the industry, it helps the researcher to better understand complex phenomenaand build-up decision models.

In this presentation, we will show how this technique can be used to tackle market researchproblems such as driver analysis. In the world of consumer goods, a products overallappreciation is under the influence of many potential factors (e.g for food: does it taste good? Isit healthy? Is it easy to prepare?): modelling the consumers evaluation process is thus relevantin order to identify and weigh the drivers of liking.

Based on a real case study (15 baby food products tested in blind, using the same procedure),we will review the different steps carried out for the modelling of appreciations drivers: first,unsupervised learning enables us to model the relations between all consumer statements, andprovides an overview of the data. Then, the induced network will be used to discover underlyingconcepts or main consumer dimensions with variable clustering based on the relationsstrengths. Next, the revealed latent variables are computed, and used to model consumeroverall appreciation using unsupervised learning. If needed users may use their expertise of themarket to manually tweak the model.

Reviewing a couple of examples, we will show how the model can be a valuable tool for theresearcher to identify and understand the key consumer drivers and simulate productperformances and optimization. Focus will be made on model quality check, and on the way toprevent overfitting.

To finish with, the overall procedure will be compared with Structural Equation Modelling, andthe benefits of Bayesian Networks, such as non-linear properties, or the possibility to combineunsupervised modelling and the expertise of the user will be discussed. As a perspective will beinvestigated the possibility to connect extra variables for example sensory descriptors to theconsumer model in order to make it even more operational.

Fig1. Causal Model of Overall Likingusing latent variables

0.67

0.48

0.37

0.42

0.38

Fig2. Latent variables structureCombination of consumer statements




A-5

Consumer Segmentation of BIB liking data of 12 Cabernet Sauvignon wines: A casestudy.

Chris Findlay

Compusense Inc, Guelph, Ontario, Canada

Consumer testing of beverage alcohol has a number of serious challenges. The effect ofconsumption of alcohol is a limiting factor in obtaining complete block data. Collecting consumerdata over several days affects the quality of the consumer response. By the third day, mostconsumers are behaving like trained assessors, a conclusion that is supported by the decreasein first position effect. Typically, segmentation of consumer liking data requires a completeblock. In this study, 12 Cabernet Sauvignon wines were evaluated by over 600 red wineconsumers in a 12 present 3 Balanced Incomplete Block design. Each consumer tasted 3 of thewines in a single 10 minute session, with demographic questions providing a break betweensamples. A total of 11 sessions were conducted at 5 LCBO store locations. Three approacheswere used to provide dummy variables for the missing data in each set. The average responsefor the panelist was inserted into the missing data points. The product average was substitutedin a second data analysis and finally the overall mean was used in the third data set. Eachapproach was subjected to Qannari Clustering (Senstools 3.3.1) and 3, 4 and 5 Clustersolutions were considered. Grouping of products based on descriptive sensory data provided anexternal validation of the selection of sensory segments. A four cluster solution using thepanelist mean produced clusters that were well explained by the sensory contrasts.

SENSOMETRICS 2008


TECHNICAL SESSION B

Descriptive Analysis Data

Kernon Gibes, Session Chair

19TECHNICAL SESSION BDescriptive Analysis Data

B-1

Panel Concordance Analysis (PANCA)

Eduard Derks

DSM Food Specialties, Delft, Netherlands

QDA-panels (Quantitative Descriptive Analysis) are frequently used in the food industry toobtain descriptive sensory profiles (e.g. flavour, texture, appearance) to support productdevelopment, manufacturing and communications. The descriptive sensory profiles arecommonly obtained by computing (weighted) average profiles across panellists and replicates,assuming good mutual agreement between the panellists on the attributes used.

Good agreement (consensus or concordance) can be achieved by extensive selection andtraining procedures and is one of the major reasons why QDA-panels are often considered asexpensive. It is obvious that poor consensus does not allow for averaging across modes andcalls for additional re-training efforts and/or different data-analysis strategies. These methodsare however still a bit academic (read: not standard ) and limitedly available for panel leadersin industrial practice.

This paper proposes Panel Concordance Analysis (PANCA) as a tool for panel leaders toidentify disconsensus between the panellists on the sensory attributes used. PANCA constitutesa combination of a descriptive model, like Principal Components Analysis (PCA) with additionaldisconsensus restrictions. PANCA summarizes the sensory data ([products x assessors] xattributes ) by a low rank approximation penalized for disconsensus (disagreement) betweenthe panellists.

When all assessors agree on the sensory attributes used, the disconsensus penalty will have anegligible effect. However, if the assumption of good-consensus (by applying the disconsensuspenalty) is not supported by the data, considerable residual errors will arise for eachdisagreeable sensory attribute. Consequently, PANCA can be used to identify difficult sensoryattributes or even poor / deviating panellists which requires further training or could call for analternative data processing strategy. Theory and applications will be explained by means ofsome real-life examples from industrial sensory practice.

B-2

Using Range Voting Analysis Techniques for Odor Profiling Data

Gregory Keep, William Raynor

Kimberly-Clark Corp., Neenah, Wisconsin, United States

The Mnemonic Theory of Odor Perception (Stevenson & Boakes 2003) envisions neuronalsignals in the brain being matched against encodings previously stored in memory. The specificmemory encodings involved, together with their degree of activation, subsequently give rise tothe perception of the originating odor stimulus. This process relates well to elements of well-known odor profiling methods, such as those that make use of either discrete odor matchingtasks or odor similarity ratings. In order to fully parallel the processes postulated by theMnemonic Theory, however, it is necessary to combine both elements in a single protocol.

We will describe a sensory profiling method which consists of test odors being evaluated in twosteps. The sensory judges (1) select matching odors from a large standardized pool of odornote exemplars and (2) rate the similarity of each against the test odor. This is performed afteran overall intensity rating is made of the test odor.

The analyses of these data require specialized techniques, as each sensory judge is free toselect a different subset of matching odors. We have thus borrowed methods normally used forRange Voting data which address just this type of situation. Weighting functions are generatedfrom the results which allow for the generation of profiling scores that can be analyzed usingstandard general linear models. We will describe this statistical approach using examples thatdemonstrate the utility and reproducibility of the odor profiling data.

Reference:

Stevenson RJ., Boakes RA. 2003. A mnemonic theory of odor perception. PsychologicalReview 110:340364.




B-3

Napping by modality: a happy medium between analytic and holistic approaches

Johann C. Pfeiffer, Chantal C. Gilbert

Campden & Chorleywood Food Research Association, Chipping Campden, United Kingdom

The Napping technique (Pags 2005) is a holistic method of data collection which requires

assessors to position products on a two dimensional surface (typically a large sheet of paper)according to similarities and differences. Following aggregation of the individual maps, actualproduct relationships can be inferred from their positions and distances on the consensus planerepresentation, similar to the PCA output from conventional profiling. An issue, however, ariseswith its application to descriptive panellists. Because the evaluation is global, panellists need toabandon their usual habit of having to separate product properties into independent sensoryattributes. This may not be desired considering the amount of training required to teach thisanalytical approach. This study examined a potential alternative, Napping by modality (or partialNapping), whereby assessors are asked to produce several plane representations according toeach of the sensory modalities (appearance, odour, flavour and texture). In doing so, panellistswould work semi-analytically keeping the advantages of the original Napping method (simplicityand rapidity) but being closer to conventional profiling (evaluation separated by modality).

A three-stage evaluation was conducted on eight samples of strawberry yoghurts: GlobalNapping, four partial Napping sessions and a QDA-type conventional profiling. UsingHierarchical Multiple Factor Analysis (HMFA, Le Dien and Pags 2003), it was possible toinvestigate the correspondence between the four individual representations with regards to theconsensus map. The results showed good agreement, further confirmed by the calculated RVcoefficients. If both Napping techniques led to similar discrimination, the main advantage ofpartial Napping over global Napping concerned the amount of information generated. Withpartial Napping, four individual maps were obtained (product positioning and associateddescriptors), which led to improved interpretation of the differences between samples,considerably enhancing knowledge of the sample set. The descriptions were also more specific(more words generated) since the panellists only focused on one modality at a time. Whencomparing the three methods using HMFA, Napping by modality proved to be more similar toconventional profiling (RV=0.88). Overall, the results confirmed the initial hypotheses that partialNapping would be closer to conventional profiling than simple Napping. It revealed moreinformation on specific relationships between products and was more appropriate to the trainedpanel both in terms of approach and opportunity to express differences and descriptions.

References:

Le Dien, S. and Pags, J. (2003) Hierarchical Multiple Factor Analysis: application to thecomparison of sensory profiles. Food Quality and Preference, 14, 397-403.

Pags, J. (2005) Collection and analysis of perceived product inter-distances using multiplefactor analysis: Application to the study of 10 white wines from the Loire Valley. Food Qualityand Preference, 16, 642-649.

B-4

Accounting for scaling differences in sensory profile data

Per Bruun Brockhoff, Niels Axel Sommer

Technical University, Denmark., Lyngbyd, Denmark

Scaling differences will often constitute a non-trivial part of the assessor-by-product interactionin sensory profile data. In the standard mixed model analysis of variance approach for statisticalanalysis this variability will enter into the resulting assessor-by-product error term but pooledtogether with potential disagreement variability. Statistical models that explicitly identifies thescaling part of the interaction term are suggested and exemplified. The distinction betweenmodels with fixed and random scaling differences is discussed. Simplified approximate versionsof both type are expressed that can be fitted by standard mixed model software. Theoreticalconsiderations within the models and simple calculation examples show that:

The fixed scaling effect approach corresponds to removal of the scaling variation

The random scaling effect approach corresponds to Scaling variation enters the errorterm:

Products close to each other have low contrast standard error.

Products far way from each other have high contrast standard error.

The two approaches can give quite different results in terms of significance level and standarderrors and they have two clearly different interpretations both of which may be of relevance tothe sensory practitioner. A special but important detail is that individual scalings in such modelsmay be as well positive as negative - a novel approach to distinguish between these twosituations will be presented.

The model approach is closely related to the so-called assessor model, Brockhoff andSkovgaard (1994). This was introduced as a tool to analyse univariate sensory profile data. InBrockhoff (2003) a more comprehensive approach using different models around this themewas presented as a tool to investigate various univariate panelist performance features. Acommon approach is to look for a proper pre-processing of the given data. For univariateanalysis of sensory profile data, the issue of pre-processing boils down to identifying and mayberemoving the variation due to pure scaling effects from the error. In Romano et al. (2008) astandard mixed model ANOVA was carried out on the pre-processed data to investigate theeffect of the scale pre-processing on the data.

From a purely statistical random effect point of view, the random scaling effect approach seemsmore justifiable than the fixed approach. But within the sensory application there may be roomfor the alternative analysis and interpretation given by the fixed approach. This paper, at least,gives the possibility for the community to be able to have a discussion of this issue based oninsight more than just gut feelings.

References:

Brockhoff, P.B. (2003). Statistical testing of individual differences in sensory profiling. FQP 14,525-434.

Brockhoff, P.M. and Skovgaard, I.M. (1994) Modelling individual differences between assessorsin sensory evaluations. FQP 5, 215-224.

Romano, R., Brockhoff, P. B., Hersleth, M., Tomic, O., Ns, T. (2008). Correcting for differentuse of scale and the need for further analysis of individual differences in sensory analysis. FQP19, 197-209.




B-5

Automated and visual mixed model ANOVA of sensory profile data.

Niels Axel Sommer, Per Bruun Brockhoff

Technical University, Denmark, Lyngby, Denmark

The new R-package Sensmixed offers automated proper mixed model analysis of 4-waysensory data where two product subfactors substitute the parent product factor in the simplermore common 3-way analysis. The 4-way setup is not uncommon though and it is important toconstruct proper tests to infer correctly about the importance of the two subfactors and theirinteraction.

With Sensmixed one gets an automated visually oriented report of the results showing theinformation gained by taking into account the decomposition of the parent product factor. This isdone by simultaneously reporting the relevant F-tests in the 4-way analysis and the F-tests in a3-way analysis where the product sub structure is disregarded. More specifically a two-layeredbar plot with bars for the 3-way F-test statistics for all attributes are overlaid by bars for the 4-way F-test statistics all of which are coloured according to their respective p-values.

Some of the the key questions that will be answered in a direct visual in the 4-way setting are(call the product factors A and B):

1. Is there any product-by-assessor interaction? And if so, is it primarily due to the A or the Bfactor?

2. Is there any product-by-session interaction? And if so, is it primarily due to the A or the Bfactor?

3. Are there any product differences?

4. Do A and B interact?

5. Are the any main effects of A and B?

The approach for properly answering questions 3, 4 and 5 depend on the answers of 1 and 2.This is automated in the package. The analysis performed with this package is robust in thesense that it handles unbalanced data and missing observations to the same degree as doesthe inbuilt R-function lme. This is the inbuilt mixed model R-function of choice in Sensmixed andit is applied subsequent to a subtle automated strategy involving purely fixed effect models todetect and initially remove some non-significant terms that potentially cause detrimentalproblems regarding the execution of lme-models in R.

To test the robustness regarding the optimal model convergence in cases of missing data acompletely balanced 4-way design dataset comprising of 192 observations for each of 15attributes has been randomly depleated for observations and it is shown that attributes with upto 20-25% missing observations often can be handled even in the 4-way analysis. Secondarilythe findings for these attributes using Sensmixed are discussed and a way of expanding the 2-way product structure analysis to also handle a 3-way product structure is proposed.

SENSOMETRICS 2008


TECHNICAL SESSION C

Linking Data Sets Correlations

Jean-Franois Meullenet, Session Chair

25TECHNICAL SESSION CLinking Data Sets Correlations

C-1

Discrimination tests with sureness judgements: Comparison of Thurstonian and R-Indexanalysis

Graham Cleaver

Unilever R&D, Vlaardingen, Netherlands

Adding sureness ratings to a discrimination task, for example with the A / Not A test, potentiallyincreases the power of the test. Used appropriately, this will increase the sensitivity of the testand efficiency of research. When used routinely as an alternative to less efficient testmethodologies, the reduced replication required to achieve the same desired sensitivity canbring considerable cost savings. However the sureness ratings add another level of complexityto the sensory model and it is important to address the issues involved if the modified test is tobe used correctly. This is the focus of this presentation, in terms of what can be achieved nowand directions for further research.

There are two types of model which are frequently used to analyse results from an A / Not-Atest with sureness. A Thurstonian model may be applied, in its standard form assumingunderlying normal distributions for perception, and this approach provides a measure of the sizeof the product difference in terms of the d (d-prime) value. Estimates of the perceptualboundaries between sureness categories are also obtained providing insights on the scoringprocess. The model fitting process, for the equal and unequal variance models, will beillustrated using procedures available in SAS.

Alternatively, and without making assumptions about the nature of the underlying perceptualdistributions, the same data can be analysed to calculate an R-Index as the measure of thedifference between the products. There are, however, different methods in common usage forperforming the significance test of the R-Index. A further complication is that the power of thetest is influenced by the way in which the subjects use the sureness scale, making conventionalpower calculations difficult.

A simulation-based approach is presented here for comparing the efficiency of the Thurstoniananalysis and the alternative analyses of the R-Index, showing, for example, that one commonly-used R-index analysis is overly-conservative and typically requires 35% greater replication toachieve the same power. The results are presented in the form of power charts for use in thecontext of difference testing and with corresponding charts for use with similarity testing.

Some problems remain, notably finding the best way of understanding and modellingdifferences between individuals, both in terms of sensitivity to differences between products andof different usage of the sureness scale. This is a modelling challenge but also a potentialsource of new insights on perception of product differences. A simplistic approach is presentedhere together with potential new directions for research.



C-2

A Variation on External Preference Mapping

Dave Plaehn

Insights Now, Inc., Corvallis, Oregon, United States

External preference mapping (EPM) is a technique often used in product development wherelatent variables (LVs) from sensory data are used to model consumer preference data on anindividual basis. Although popular, EPM has garnered some notable criticisms and concernsranging from poor fit of the preference data to possible overfitting in individual models toabsence of or problems with specific optimal product predictions. A new method, based onEPM, is proposed to try to remedy these concerns. The goal of the new method is give anestimate of the optimal product profile in the original sensory space for a given consumer group.To help insure the accuracy of the optimum estimate, the method typically uses more LVs thantraditional EPM. To help achieve a robust estimate the method uses the root-mean-square errorof cross-validation (RMSECV) in individual model selection and the R

2 of validation in

determining which individual models to retain. Noting that individuals may differ as to therelative importance of given latent variables, individual model selection occurs not only for agiven LV range but for a number of ranges, e.g. 1-2, 1-3, 1-4, , 1-Amax. After individual modelsare created and then culled based on R

2 of validation, standard optimization techniques can be

applied to estimate product optima. The optimization process tries to find a sensory profile thatmaximizes predicted group preference. The latter is taken to be the mean (or some othermeasure of location) of all the individual model predictions associated with that group. In theexamples given, the optimization approach used allows for inputting of various constraints tohelp avoid model extrapolation to further help maintain accuracy and robustness. The method isapplied to a publically available data set and shown to model the preference data well in both acalibration and a validation sense. In addition, given enough latent variables, the methodretained a large proportion of the individuals in the given populations. Optima for various LVranges are examined for consistency.


C-3

Comparison of three methodologies to identify drivers of liking of milk desserts

Gastn Ares, Ana Gimnez, Cecilia Barreiro, Adriana Gmbaro

Seccin Evaluacin Sensorial. Facultad de Qumica. Universidad de la Repblica, Montevideo,Uruguay

One of the main objectives of consumer research is to understand what the key sensory driversof liking of the product of interest are. The aim of the present work was to compare results fromthree different techniques to identify drivers of liking of milk desserts: internal, externalpreference mapping, and an open-ended question.

Eight samples of vanilla milk desserts were formulated varying sugar, vanilla, starch,carragenan and fat concentrations, following a L82

7 Taguchi design. A panel of trained

assessors evaluated 9 flavour and texture descriptors. A consumer study was also carried outwith 80 people who regularly consumed milk desserts. Consumers evaluated the samples usinga 9-point hedonic scale and were also asked to provide up to four words to describe each of thesamples using an open-ended question. Internal and external preference mapping techniqueswere applied. Data from the open ended questions were qualitatively analysed considering wordsynonymy. Correspondence analysis was performed on the data from this task in order tovisualize the relationship between concepts and samples.

According to both internal and external preference mapping, the main drivers of liking of theevaluated milk puddings were related to texture Creaminess, Thickness, Stickiness and Mouth-coating.

The first two dimensions of the correspondence analysis explained 80.69% of the variability ofthe experimental data. The evaluated milk desserts were sorted into two groups. Oneassociated with words like Delicious, Creamy, Thick, Good taste, Sweet and Soft, showing thatthese sensory attributes were drivers of liking for the evaluated milk desserts. Another group ofsamples was characterized using terms like Milky flavour, Disgusting, and Not very thick.

The three evaluated methodologies provided similar results. However, the proposed open-ended questions showed several advantages over traditional preference mapping techniques toidentify drivers of liking. First, no correlations were needed in order to link sensory attributes, asconsumers identified both liked and disliked samples and the attributes responsible for thesepreferences. Moreover, attributes relevant for consumers were identified using consumerslanguage. One of the main limitations of this technique is the analysis of open-ended questions.However, consumers mainly used individual words, which made the analysis much easier thanthe analysis of larger phrases.



C-4

Two-step procedure for classifying consumers in a L-structured data context

Isabella Endrizzi1, Flavia Gasperi

1, Evelyne Vigneau

2

1IASMA Research Centre, Agrifood Quality Department, Via E. Mach,1, 38010 S. Michele (TN),

Italy, 2ENITIAA/INRA ,Unit de Sensomtrie et de Chimiomtrie, , la Graudire BP 82225

Nantes Cedex, France

The present work proposes a method based on CLV (Clustering around Latent Variables) foridentifying groups of consumers in L-shape data. This kind of data-structure is very common inconsumer studies where a panel of consumers is asked to assess the global liking of a certainnumber of products and then, preference scores are arranged in a two-way table Y. Externalinformation on both products (physical-chemical description or sensory attributes) andconsumers (socio-demographic background, purchase behaviours or consumption habits) maybe available in a row descriptor matrix X and in a column descriptor matrix Z respectively. Theaim is to automatically provide a consumer segmentation where all the three matrices play anactive role in the classification, getting as homogeneous groups as possible from all points ofview: preference, products and consumer characteristics.

The proposed clustering method is compared with the procedure adopted in Esposito Vinzi etal. (2007), where a double PLS regression is use to capture all the information contained in theL-structure, and then, consumer classification is performed on the columns of the finalcomponent matrix.

The two approaches are illustrated on a real data-set from a preference study on a new line offresh juices based on berry fruits. The hedonic ratings from 72 consumers on 25 fruit juicemixes were explained with respect to the product in terms of some basic compositionalparameters and consumer socio-demographic information, purchase behaviour andconsumption habits.

Reference:

Esposito Vinzi, V., Guinot, C. & Squillacciotti S. (2007) Two-step PLS Regression for L-Structured Data: an application in the cosmetics industry. Statistical Methods and Applications,Physica-Verlag, 16(2), 263-278.

Keywords: Consumer segmentation; Preference data; Clustering of variables; PLS regression.


C-5

Analysing four-way sensory data resulting from individual vocabulary profiling: Acomparison between HMFA and PARAFAC2

Gatan Lorho

Nokia Corporation, Helsinki, Finland

This presentation considers the application of Hierarchical Multiple Factor Analysis (HMFA) andParallel Factor Analysis 2 (PARAFAC2) to a multiway dataset resulting from a sensoryevaluation made with an individual vocabulary profiling method similar to Flash Profile. Sensorydata is usually represented with the three modes Product, Assessor and Attribute but thepresent application relates to a set of methods to reproduce sound over headphones, whichrequires the introduction of a fourth mode. In this case, the stimuli depend on the audio materialchosen for the sensory evaluation, e.g. several music clips of different genres. This selectionrepresents only a small sample of a larger population but it ensures that all the perceptualaspects relating to these sound reproduction systems are covered. In practice, the systemshave to be evaluated for each music clip and this leads to a sensory dataset best described asa four-way structure comprising the modes System, Music clip, Assessor and Attribute. The aimof the analysis is then to compare the sensory characteristics of the systems in a global sensebut also to assess the effect of the audio clips on the different systems.

HMFA and PARAFAC2 are two methods allowing a direct estimation of the mode Music clip bypreserving the structure of the full dataset. Three-way analysis methods such as GPA, Tucker-1, MFA or STATIS all require the unfolding of one of the modes, which complicates the separateanalysis of the four modes. In HMFA, several groups of variables organized in a structuredhierarchy can be analysed simultaneously. This PCA-based method works by balancing thecontribution of the different groups at each level of the hierarchy. In the present case, the firstlevel considers each individual profile separately (i.e. the matrix System by Attribute for agiven assessor and a single music clip), the second level groups the sensory profiles by musicclip and the top level covers the whole dataset. HMFA also offers means to study therelationships between the systems, attributes, assessors and music clips. An alternativeapproach to handle n-way datasets is the PARAFAC2 model, which is a modified version ofPARAFAC allowing for differences between variables in one mode. With individual vocabularyprofiling data, the cross-product of the matrix System by Attribute is used for the model fitting.Both versions of PARAFAC aim at describing the multiway structure of the data and theoutcome of the PARAFAC2 model in the present case comprises a set of loadings for each ofthe four modes.

We compared a two-component model of HMFA and PARAFAC2 in this study and obtainedsimilar results both in terms of system configuration and influence of the different music clipsalong the two underlying dimensions. The mode Attribute is handled differently in the twoanalysis methods, PARAFAC2 offering a more concise representation of the individualattributes. However, the semantic interpretation of the latent structure in the two modelscompares relatively well when the rotational freedom specific to PCA-based methods isaccounted for in the HMFA model explanation.

SENSOMETRICS 2008


TECHNICAL SESSION D

Qualitative Data

Todd Renn, Session Chair

31TECHNICAL SESSION DQualitative Data

D-1

A procedure for statistical treatment of free sorting data

El Mostafa Qannari1, Veronique Cariou

1, Eric Teillet

2, Pascal Schlich

3

1ENITIAA/INRA, NANTES, France,

2Lyonnaise des Eaux, Dijon, France,

3INRA/CESG, Dijon,

France

The interest for the free sorting procedure in sensory evaluation is gaining ground as it providesa quick and reliable means to assess similarities among a set of products (stimuli) by a panel ofassessors or consumers (subjects). We discuss an approach of analysis which is in the spirit ofDISTATIS (Abdi et al., 2007). However, we adopt a specific procedure of normalisation of thedata which entails that the method of analysis fits within the framework of dual scaling andcorrespondence analysis which is precisely the same framework as the method MDSORTproposed by Takane (1981). Moreover, instead of running the STATIS method, we undergo amethod of analysis which allows for individual differences in the stimulus configuration:Common Components and Specific Weights Analysis (CCSWA). This method of analysis is akinto INDSCAL type of algorithm. Thus the general procedure of analysis bears some resemblanceto IDSORT which was designed by Takane (1982) as an extension of MDSORT to three waydata to take account of individual differences in sorting data.

Let us assume that subject j (j=1, , J ) has sorted n stimuli into pj clusters byconsidering that stimuli in each cluster are perceived as similar. Let us denote by Xj (nxpj) thematrix of dummy variables indicating for each stimulus the group to which it belongs. In a firststep, Xj (j=1, , J) is column-centred and normalized by dividing each column by the squareroot of the number of stimuli in the cluster associated with the column under consideration.More formally, the new centred and standardized dataset Y j is derived from X j by

21

= jjT

j DX)n

uuI(Y , where I is the identity matrix, u is the vector of size n whose

components are equal to 1 and Dj is the diagonal matrix whose diagonal elements are equal tothe numbers of products in the various clusters formed by subject j. This standardization isusual with categorical data, particularly within the framework of correspondence analysis andcan be backed up by several considerations. Thereafter, the matrix of scalar products between

stimuli is computed as T

jj

j

j YYp

W1

1

= . The term 1jp in the denominator is

introduced in order to set the subjects on the same footing as regards the number of clustersthey have chosen to partition the stimuli in. Similarly to DISTATIS, the agreement between twosubjects j and j can be assessed by computing the RV coefficients between Wj and Wj and wecan prove that this coefficient is proportional to the khi-squared statistic of the contingency tablewhich cross-tabulates the clusters of subject j with those of subject j. Thus, the interpretation ofthis index is straightforward. In order to depict the relationships between stimuli, we propose toperform CCSWA on the matrices of scalar products. Basically, this consists in approximating

each matrix Wj by a matrix Q jQT, where Q contains the (orthogonal) components which are

assumed to be common to all the datasets at hand and j is a diagonal matrix which containsthe saliences. These saliences are specific to subject j and reflect the importance he or shebestows on the various components. The relationships among stimuli are depicted on the basisof the common dimensions (stimulus space) and the relationships among subject are reflectedby the saliences (subject space).

The general approach of analysis is illustrated on the basis of data from a case study whichinvolved 389 consumers who were asked to sort 12 mineral and tap waters. The outcomes arecompared with those of DISTATIS.



D-2

Free listing: a method to gain initial insight of a food category.

Guillermo Hough, Daniela Ferraris

Instituto Superior Experimental de Tecnologa Alimentaria, Nueve de Julio, Argentina

Free listing is a simple but powerful technique which has been widely applied to anthropologicalstudies. In free listing you ask informants to list all the X you know about or inquire what kindsof X are there; where X might be what is eaten at breakfast time, movie stars or dairy products.

In the present study 184 15-18 year old adolescents from a small town in Argentina were askedto list all the fruits they knew, whether theyd tasted them or not and whether they liked them ornot. Half the population was middle-upper class and the other half was lower-middle class.Approximately half were girls and half were boys.

From free listing, summary statistics can be calculated. An average of 17.4 fruits was listed, withmiddle-upper class averaging significantly higher number of fruits than lower-middle class: 18.1and 16.6, respectively. Fruits listed most frequently were: bananas, apples, grapes, oranges,peaches, kiwi and strawberry. Some of these fruits were seasonal at the time of the survey.

One of the interesting possibilities of free listing is that it can be hypothesized that elementslisted close together by a respondent are in some way more associated than elements listedfarther apart. The relative position of the fruits listed by each respondent can be analyzed bycluster analysis and/or multidimensional scaling. As respondents are not usually asked whythey used their particular listing order, the researcher has to try and discover the reasonsbehind clusters and groupings. As an example, the figure shows the cluster analysis of thelisting by lower-middle class adolescents.

0.4 0.6

nectarine

0.5

medlar

orange

mulberry

melon

mango

mandarin

apple

lemon

kiwi

fig

sour _cherry

pomegranate

rasberry

strawberry apricot

watermelon

peach

grapefruit

coco

plum

cherry

banana

pineapple

grape

pear

kumquats

1.0 0.9 0.8 0.7


D-3

A novel Factorial Approach for analysing Sorting Task data

Marine Cadoret, Sbastien L, Jrme Pags

Agrocampus, Rennes, France

Sorting task or categorization is a cognitive process often used to collect data, in particular insensory analysis where one seeks to describe products according to their sensory properties.This task consists in asking assessors to group products in function of their sensoryresemblances. Following this task, a verbalization task can also be asked to describe thegroups and to supplement the original data: in this case we will speak about qualifiedcategorization. Categorization is becoming more and more popular since it does not need to beperformed by trained assessors.

The object of this talk is to present a new approach to analyze categorization data called FASTthat stands for Factorial Approach for Sorting Task data. This approach, based on multiplecorrespondence analysis (MCA), provides an optimal representation of the products, an optimalrepresentation of the consumers, which are to be interpreted jointly. It provides also elements ofvalidation based on confidence ellipses. In the case of qualified categorization, it provides anoptimal representation of the words which is directly linked to the representation of the productsand the consumers.

The FAST approach will be illustrated by an example where 98 consumers were asked to group12 luxury perfumes: Angel, Aromatics Elixir, Chanel 5, Cinma, Coco Mademoiselle, Linstant,Lolita Lempicka, Pleasures, Pure Poison, Shalimar, Jadore (perfume spray), Jadore (toiletwater). They were also asked to characterize each group they provided (qualifiedcategorization). The data can be put in a table with 12 rows and 98 columns, in which each rowi corresponds to a perfume, each column j corresponds to a consumer, a cell (i, j) correspondsto the words associated with the group to which perfume i belongs to for consumer j. Eachconsumer j can thus be assimilated to a qualitative variable with Kj categories, where Kj denotesthe number of groups used by consumer j during his categorization; each one of his Kjcategories corresponds to the sequence of words he used to describe the groups.

Such data can be analyzed with MCA which provides a representation of the 12 perfumes thatcan be interpreted on the basis of the words used by the 98 assessors to characterize theirgroups.

An optimal representation of consumers is obtained using the equivalence between MCA andmultiple factor analysis (MFA) in which each consumer corresponds to a group.

To obtain the confidence ellipses around the products, the idea consists in using resamplingtechniques on our initial set of consumers using random draws with replacement and inprojecting the resampled consumers as supplementary variables. The categories (i.e. wordsused by the consumers to characterize their groups) associated with our virtual consumers onceprojected, we calculate the new coordinates of the products according to the barycentricproperties of MCA. This process is then reiterated several times in order to get confidenceellipses which include 95% of the representations of the virtual products thus obtained. Theresults, obtained using the software R and the SensoMineR package, will be discussed duringthe presentation.



D-4

An original use of Pearsons correlation to construct a unique assessment procedurefrom individual ones for dynamic hedonic tests of cars

Celine Astruc1, David Blumenthal

1, Julien Delarue

2, Marc Danzart

2, Jean-Marc Sieffermann

2

1Renault, Guyancourt, France,

2Agroparistech, Massy, France

In order to predict the consumer liking, we need to have an appropriate driving procedure toassess future cars. We thus want to frame a route that reflects the consumer contexts of use.To get closer to a real test drive as consumers do with a car dealer, we developed a behavioralstudy included the consumers directly in the process of constructing the driving procedure. Theaim of this study was to observe the consumer choices of roads for car assessment under realdriving conditions. 64 participants were free to drive anywhere and as long as they wanted inorder to make their own judgment on the cars.

Four steps were handled to frame a unique driving procedure closest to the individual ones.First, we characterized individual routes by quantified data. Then, we built several virtual routes.The third step consisted in choosing the virtual route reflected more the different consumerchoices. The last step, and the most difficult one, was to transfer a virtual route to a real contextof driving.

The individual routes chosen by the 64 drivers were described by the global driving time, by thedriving time on 8 different types of roads and by the occurrence of 100 isolated and momentaryroad events. From the characterization of the individual procedures, we built several commonprocedures as close as possible to the choice of the drivers. One procedure was the averageconsumer choice of roads and events, another one was the median consumer choice. Twoother procedures contained the maximum driving time on different roads and the maximumoccurrence of road events under the constraint of global driving time. The first one was obtainedby simple treatments, while the second one was constructed using genetic algorithms. Thecomparison of these different virtual common routes was based on one index, which estimatesthe relevance of each common route for every individual route. The selected virtual route wasthe one with the highest average index.

Index = N road elements chosen by the consumer and included in the unique virtual route / N road elements chosen by the consumer

The aim of the last step was to set up the unique procedure under real driving conditions. Weused Pearsons correlations to link the selected virtual route to all the real individual routes. Thistreatment helped us select the individual procedures correlated (


D-5

Mixed Logit Modelling of Paired preference data

Mark Wohlers

Hortresearch, Auckland, New Zealand

Analysis of paired-preference data with the no preference option has in the past been less thansatisfactory. For example, two methods for dealing with such data have been to exclude thedata where there was no preference or equally allocate these responses to the other twopreference options and then analysing the data with a standard logistic regression. Analternative approach is to fit a conditional logit model, which is a multinomial extension of thestandard logit. However one key assumption that this model makes is the Independence ofIrrelevant Alternatives (IIA), which may not be valid in the paired preference setting with the nopreference option. With paired preferences IIA implies that the relative odds between choosingno preference and product A do not change depending on B, the other product tasted. Ifproduct B is similar to A then the odds of choosing no preference would increase while theodds of A would decrease. The opposite would happen if we chose B to be very different fromA, and so the relative odds of no preference to A would depend on product B violating IIA.However, advanced logit models such as the mixed logit relaxes the IIA assumption, and mayoffer a more satisfactory approach to data analysis. Using data relating to consumerpreferences of fruit flavoured beverages, this presentation compares results obtained withtraditional (and basic) approaches to analysis of paired preference data, to those obtained withdifferent logit models. When the IIA assumption appeared reasonable the logit methodsproduced similar results to one another, and to the traditional approaches. However when theIIA was violated the estimates from the mixed logit were superior and improved model fit asmeasured by Akaike's Information Criterion (AIC) and the Bayesian generalisation DIC.

SENSOMETRICS 2008


TECHNICAL SESSION E

Potpourri

Isabelle Lesschaeve, Session Chair

37TECHNICAL SESSION EPotpourri

E-1

Influence of visual masking technique on the assessment of two red wines by trainedand consumer assessors

Carolyn Ross1, Jeffri Bohlscheid

2, Karen Weller

1

1Washington State University, Pullman Washington, United States,

2University of Idaho,

Moscow Idaho, United States

During sensory evaluation assessments, visual masking techniques are frequently employed todisguise color differences between samples and minimize perceptual bias. Particularly in wine,the impact of these masking techniques on panelist evaluations has not been well studied. Theobjective of this study was to study the influence of visual masking techniques on the aromaand flavor assessment of two red wines and observe the impact of these techniques on trainedand consumer sensory panels. Specific masking techniques included 1) blue wine glass/whiteillumination, 2) clear glass/red illumination, 3) clear glass/white illumination. Ten panelists weretrained to recognize seven aroma and flavor attributes while consumer panelists (n=80)evaluated attributes and liking. For the trained panel, the visual masking technique affected

only perceived spicy flavor of Syrah (p0.05), with the clear glass/red illumination resulting inmore intense spicy flavor compared to the other two conditions. Principal components analysisshowed that for the two red wines evaluated by the trained panel, red illumination resulted inhigher spicy attributes and perceived astringency while wines served in blue wine glasses werehigher in perceived astringency. For the consumer panel, red illumination resulted in wineshigher in perceived astringency and blue wine glasses resulted in wines higher in perceivedflavor liking. These results indicated that d visual masking techniques may influence bothtrained and consumer panel evaluation of aroma and flavor attributes of red wine. However,beyond red wine, this study makes the larger point that the choice of masking technique doesimpact sensory evaluations.



E-2

The application of Thurstonian models for replicated difference tests

Rune H.B. Christensen, Per B. Brockhoff

Technical University, Denmark., Lyngby, Denmark

We propose Thurstonian model for replicated difference tests (K-AFC, duo-trio or triangle) inwhich several subjects participated in the experiment. The model will provide insight into thedistribution of subjects by estimation of the following quantities: 1) The average subject specificd-prime and the standard error of this estimate 2) The variance in d-primes among subjects inthe population 3) The estimated d-prime for each subject 4) The probability of each subjectbeing a discriminator and 5) An estimate of the probability that a random subject from thepopulation is a discriminator.

The proposed model is (in statistical terms) called a conditional model, and we contrast thismodel to what is known as marginal models including the binomial, the binomial mixture and the(corrected) beta-binomial models. The proposed model assumes that the Thurstonian modelholds for each subject and we show that the marginal models implicitly assume that theThurstonian model holds for a group of subjects, hence the psychometric functions assumed bythe classes of models are different. Further we show that the marginal psychometric functionsdepend on the heterogeneity in d-prime among subjects. In general, the marginal andconditional estimates of d-prime differ.

The proposed model allows some subjects to have a d-prime of zero and a specific continuousdistribution for subjects with a d-prime > 0. We have found the proposed distribution to be verytenable in analyses performed so far. The model assumes that conditional on the subjectspecific d-primes, the number of correct answers follows a binomial distribution. Further the two-class binomial mixture model (Kunert 2001 and Brockhoff 2003) and the generalized linearmodel formulation of the discrimination tests (Brockhoff and Christensen, 2008) are shown to bea special cases of the proposed model.

We re-analyse two datasets from the literature (2-AFC (Bi and Ennis 1998) and triangle(Duineveld and Meyners 2008)) to illustrate the proposed model. We find that the proposedmodels fits the data significantly better than previously proposed models and provides severalmeans of interpreting the data. We compare our analyses with those of marginal models anddiscuss differences in interpretation along with practical implications for applied sensoryscientists. Lastly we provide the free R-package SensR (Christensen and Brockhoff, 2008) withall the functionality to estimate the model, provide parameter estimates with standard errors,goodness of fit measures and a suite of illustrative plots.


E-2 (contd)

References:

Bi, J. and Ennis, D.M. (1998) A Thurstonian variant of the beta-binomial model for replicateddifference tests. Journal of Sensory Studies 13, p 461-466

Brockhoff, P.B. (2003) The statistical power of replications in difference tests. Food Quality andPreference 14, p 405-417

Brockhoff, P.B. and Christensen R.H.B. (2008) Thurstonian models for sensory discriminationtests as generalized linear models, Manuscript for Food Quality and Preference

Christensen, R.H.B. and Brockhoff, P.B. (2008). SensR: An R-package for sensorydiscrimination data. Poster at 2008 Sensometrics Conference, Ontario, Canada.

Duineveld, K. and Meyners, M. (2008) Hierarchical Bayesian analysis of true discriminationrates in replicated triangle tests. Food Quality and Preference 19, p 292-305

Kunert, J. (2001) On repeated difference testing. Food Quality and Preference 12, p 385-391



E-3

Influence of experimental design in paired comparison studies:

How to reduce duration of the experiments?

Emmanuelle Diaz1, Michel Semenou

2, Philippe Courcoux

2, Pauline Faye

1

1PSA Peugeot Citron, Vlizy Villacoublay, France,

2ENITIAA, Laboratoire de Sensimtrie et de

Chimiomtrie, Nantes, France

Consumer preference analysis can be performed by using paired comparisons experiments,which consist in comparing pairs of products on a given criterion. The procedures involved areeasily performable by consumers and provide a good quality of discrimination between products(Gacula and Singh, 1984; Gacula, 1993; O'Mahony et al., 1994). In spite of these advantages,the number of comparisons to be made increases very quickly with the number of productstested, that is to say t(t-1)/2 comparisons for t products. When lots of products are tested, theexperiment can last long, resulting in high expenditure and be hardly performable in practicewith consumers. This disadvantage confirms why paired comparisons are not so usedpresently. Andriambolona et al. (1997) has shown the possibility to present only 1/3 pairswithout degrading the main results on food products. The aim of this study is to confirm this rateon other products.

Two experiments were conducted: 7 automotive textiles and 12 car shapes were pairwisecompared, on preference by 84 subjects for textiles, on modernity by 110 subjects for cars.Pairs of products, i.e. textiles or cars, were presented according to Kratchick block design(David 1988), which can be degraded by subjects keeping the balanced block design globalproperty. Each participant evaluated all pairs, 21 for textiles, 66 for cars.

For each experiment, responses were analyzed performing the Bradley-Terry Luce model(Bradley and Terry, 1952), firstly for the complete design and then for all the incompletesituations. For each level, results were compared with the whole data set results according tothe following two criteria: discrimination between products and consistency of scores. Then, forthe whole data set and for each incomplete design level, responses were independentlyanalyzed using latent class models (Courcoux and Semenou, 1997) and lead to a classificationof subjects in homogenous clusters and product scores for each class.

Finally, for the textiles experiment, the number of discriminated pairs, that is equal to 13 for thetotal number of comparisons (21 pairs). It decreases for less than 11 pairs evaluated bysubjects. The correlation between observed mean scores and Bradley Terry scores, close toone for the complete design, shows that the model fits the data well. This criterion is notinfluenced by the degradation of the design. Other than that, the correlation between textilesmean scores and individual scores is low which indicates that preference is different betweensubjects and that the segmentation of panel is relevant. Four latent classes are obtained for thenumber of evaluated pairs varying from 21 pairs to 14. Under 2/3 of pairs tested, this numberdecreases like the correlation between individual scores and the participant belonging classscores. In this way under 2/3 of pairs tested, clusters cant represent all the preference trends.

Similar conclusions are obtained on the car shapes experiment results. This study shows that2/3 pairs per subject are necessary to guarantee similar results to the complete designexperiment. Thanks to these results, duration of experiments based on paired comparisonprotocol will decrease.

To complete this study, the influence of the number of participants on paired comparison resultswill be studied in the close future.


E-4

Recovery of Subsampled Dimensions by Multifactor Analysis of Projective Mapping andMultidimensional Scaling of Sorting Data

Harry Lawless, Michael Nestrud

Cornell University, Ithaca, New York, United States

In perceptual mapping, a potential problem can arise if different subjects are attending todifferent subsets of sensory attributes, a condition known as subsampling. In theory, multifactoranalysis (MFA), as an individual differences analysis, should be able to recover subsampleddimensions. Multidimensional scaling (MDS) of sorting data, as it is based on group totals, maynot. Simulations were conducted with a 2 x 2 x 2 design where simulated products differed intwo levels of each of three attributes, and subgroups of subjects attended to only two of thethree dimensions. Data from simulated placements in a projective mapping (also called nappe)task and from simulated sortings were submitted to MFA and MDS, respectively. MFArecovered the missing dimensions, assigning appropriate variance to situations where twogroups of subjects had one common and one unique attribute each, and where three groups ofsubjects each attended to only two of the three dimensions. MDS also found evidence for threedimensions as shown by scree plots of residual stress. However, neither analysis aligned thecube of the original design with the axes as they were output by the programs. This wouldpresent a significant impediment to any sensory practitioner attempting to interpret theconfigurations without additional information. Regression of simulated attribute ratings throughthe configurations (as in external preference mapping) was able to show vectors correspondingto the three important orthogonal dimensions for both kinds of perceptual maps. It isrecommended that nappe and sorting data be interpreted via this additional step of datacollection and analysis.



E-5

A Thurstonian Model for the Unspecified Hexad Test

Keith Eberhardt, Victoire Aubry, Karen Robinson

Kraft Foods, East Hanover, New Jersey, United States

In the unspecified hexad discrimination test, 3 A and 3 B samples are presented blind, andjudges are asked to sort the samples into two homogeneous groups of three. Under theguessing model, the probability of a correct grouping is 1/10. This paper uses a Thurstonianmodel to derive the psychometric function for this test, i.e. the probability of correct grouping asa function of the Thurstonian distance, d, between the A and B samples. The results showthat the hexad test is more powerful than a twice-replicated (i.e. double) triangle test, in whicheach judge also must taste 6 samples but evaluates them in two groups of three. Theoreticalpower and sample size curves are presented comparing the hexad and double triangle tests.

The superior discrimination power of the hexad might be expected intuitively when it isconsidered that, in order to perform the grouping task for a hexad test, the judge must make, ineffect, 15 pairwise comparisons among the six samples, whereas a double triangle test onlyrequires 6 pairwise comparisons. This raises the possibility that the complexity of the cognitivetask required for the hexad test may degrade the performance of judges. On the other hand,recent research has called attention to panelists' natural ability to perform sorting tasks (e.g.Cartier et al., 2006).

As an initial experiment to gauge the practicality of the hexad test, we used a trained descriptivepanel to differentiate pairs of confusable stimuli (cold beverage solutions varying inconcentration) using both hexad and double triangle tests. The data were collected accordingto a balanced design in order to avoid learning effects and bias of test type: while half of thepanel was doing the hexad test, the other half was doing the double triangle test. The sensorydistance between tested sample pairs was varied over a rang

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