Multivariate And Probabilistic Analyses Of Sensory Science Problems

by Meullenet, Jean-Franç; ois; Xiong, Rui; Findlay, Christopher J.

• ISBN13: 9780813801780
• ISBN10: 0813801788
• Edition: 1st
• Format: Hardcover
• Copyright: 2007-08-28
• Publisher: Wiley-Blackwell

Summary

Sensory scientists are often faced with making business decisions based on the results of complex sensory tests involving a multitude of variables. Multivariate and Probabilistic Analyses of Sensory Science Problems explains the multivariate and probabilistic methods available to sensory scientists involved in product development or maintenance. The techniques discussed address sensory problems such as panel performance, product profiling, and exploration of consumer data, including segmentation and identifying drivers of liking. Applied in approach and written for non-statisticians, the text is aimed at sensory scientists who deal mostly with descriptive analysis and consumer studies. Multivariate and Probabilistic Analyses of Sensory Science Problems offers simple, easy-to-understand explanations of difficult statistical concepts and provides an extensive list of case studies with step-by-step instructions for performing analyses and interpreting the results. Coverage includes a refresher on basic multivariate statistical concepts; use of common data sets throughout the text; summary tables presenting the pros and cons of specific methods and the conclusions that may be drawn from using various methods; and sample program codes to perform the analyses and sample outputs. As the latest member of the IFT Press series, Multivariate and Probabilistic Analyses of Sensory Science Problems will be welcomed by sensory scientists in the food industry and other industries using similar testing methodologies, as well as by faculty teaching advanced sensory courses, and professionals conducting and participating in workshops addressing multivariate analysis of sensory and consumer data.

Author Biography

Jean-François Meullenet, Ph.D., is associate professor of Sensory Science in the Department of Food Science at the University of Arkansas, Fayetteville, AR. Dr. Meullenet conducts research in the area of sensory science and his expertise encompasses sensory and consumer science, rheology and modeling of food perception. Rui Xiong, Ph.D., is a research scientist with the Consumer Science Insights, Unilever Home & Personal Care, Trumbull, CT, USA. Christopher J. Findlay, Ph.D., is president of Compusense, Inc., Guelph, Ontario, Canada. He is associate editor for sensory evaluation for Food Research International.

Table of Contents

Introduction	p. 3
A Description of Sample Data Sets Used in Further Chapters	p. 9
A Description of Example Data Sets	p. 9
References	p. 25
Panelist and Panel Performance: A Multivariate Experience	p. 27
The Multivariate Nature of Sensory Evaluation	p. 27
Univariate Approaches to Panelist Assessment	p. 29
Multivariate Techniques for Panelist Performance	p. 32
Panel Evaluation through Multivariate Techniques	p. 43
Conclusions	p. 46
References	p. 47
A Nontechnical Description of Preference Mapping	p. 49
Introduction	p. 49
Internal Preference Mapping	p. 49
External Preference Mapping (PREFMAP)	p. 58
Conclusions	p. 66
References	p. 67
Deterministic Extensions to Preference Mapping Techniques	p. 69
Introduction	p. 69
Application and Models Available	p. 69
Conclusions	p. 89
References	p. 94
Multidimensional Scaling and Unfolding and the Application of Probabilistic Unfolding to Model Preference Data	p. 95
Introduction	p. 95
Multidimensional Scaling (MDS) and Unfolding	p. 96
Probabilistic Approach to Unfolding and Identifying the Drivers of Liking	p. 98
Examples	p. 100
References	p. 109
Consumer Segmentation Techniques	p. 111
Introduction	p. 111
Methods Available	p. 111
Segmentation Methods Using Hierarchical Cluster Analysis	p. 113
References	p. 126
Ordinal Logistic Regression Models in Consumer Research	p. 129
Introduction	p. 129
Limitations of Ordinary Least Square Regression	p. 129
Odds, Odds Ratio, and Logit	p. 130
Binary Logistic Regression	p. 133
Ordinal Logistic Regression Models	p. 144
Proportional Odds Model (POM)	p. 144
Conclusions	p. 160
References	p. 160
Risk Assessment in Sensory and Consumer Science	p. 163
Introduction	p. 163
Concepts of Quantitative Risk Assessment	p. 164
A Case Study: Cheese Sticks Appetizers	p. 166
Conclusions	p. 176
References	p. 176
Application of MARS to Preference Mapping	p. 179
Introduction	p. 179
MARS Basics	p. 179
Setting Control Parameters and Refining Models	p. 187
Example of Application of MARS	p. 188
A Comparison with PLS Regression	p. 201
References	p. 205
Analysis of Just About Right Data	p. 207
Introduction	p. 207
Basics of Penalty Analysis	p. 208
Boot Strapping Penalty Analysis	p. 210
Use of MARS to Model JAR Data	p. 212
A Proportional Odds/Hazards Approach to Diagnostic Data Analysis	p. 215
Use of Dummy Variables to Model JAR Data	p. 220
References	p. 233
Index	p. 237
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