Multivariate And Probabilistic Analyses Of Sensory Science Problems Book

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