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.

Introduction 3Chapter 1 A Description of Sample Data Sets Used in Further Chapters 91.1 A Description of Example Data Sets 9References 25Chapter 2 Panelist and Panel Performance: A Multivariate Experience 272.1 The Multivariate Nature of Sensory Evaluation 272.2 Univariate Approaches to Panelist Assessment 292.3 Multivariate Techniques for Panelist Performance 322.4 Panel Evaluation through Multivariate Techniques 432.5 Conclusions 46References 47Chapter 3 A Nontechnical Description of Preference Mapping 493.1 Introduction 493.2 Internal Preference Mapping 493.3 External Preference Mapping (PREFMAP) 583.4 Conclusions 66References 67Chapter 4 Deterministic Extensions to Preference Mapping Techniques 694.1 Introduction 694.2 Application and Models Available 694.3 Conclusions 89References 94Chapter 5 Multidimensional Scaling and Unfolding and the Application of Probabilistic Unfolding to Model Preference Data 955.1 Introduction 955.2 Multidimensional Scaling (MDS) and Unfolding 965.3 Probabilistic Approach to Unfolding and Identifying the Drivers of Liking 985.4 Examples 100References 109Chapter 6 Consumer Segmentation Techniques 1116.1 Introduction 1116.2 Methods Available 1116.3 Segmentation Methods Using Hierarchical Cluster Analysis 113References 126Chapter 7 Ordinal Logistic Regression Models in Consumer Research 1297.1 Introduction 1297.2 Limitations of Ordinary Least Square Regression 1297.3 Odds Odds Ratio and Logit 1307.4 Binary Logistic Regression 1337.5 Ordinal Logistic Regression Models 1447.6 Proportional Odds Model (POM) 1447.7 Conclusions 160References 160Chapter 8 Risk Assessment in Sensory and Consumer Science 1638.1 Introduction 1638.2 Concepts of Quantitative Risk Assessment 1648.3 A Case Study: Cheese Sticks Appetizers 1668.4 Conclusions 176References 176Chapter 9 Application of MARS to Preference Mapping 1799.1 Introduction 1799.2 MARS Basics 1799.3 Setting Control Parameters and Refining Models 1879.4 Example of Application of MARS 1889.5 A Comparison with PLS Regression 201References 205Chapter 10 Analysis of Just About Right Data 20710.1 Introduction 20710.2 Basics of Penalty Analysis 20810.3 Boot Strapping Penalty Analysis 21010.4 Use of MARS to Model JAR Data 21210.5 A Proportional Odds/Hazards Approach to Diagnostic Data Analysis 21510.6 Use of Dummy Variables to Model JAR Data 220References 233Index 237