Multivariate And Probabilistic Analyses Of Sensoryscience Problems

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.

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.

This technical work provides a useful insight into the solution of a number of pertinent problems in sensory science. This is an excellent work for sensory specialists and challenges the reader to consider alternate strategies for handling sensory data. ( Journal of Dairy Technology, May 2009)

Foreword.
Introduction.
Chapter 1: A description of sample datasets.
1.1. White Corn Tortilla Chips.
1.2. Muscadine Grape Juices.
1.3. Fried Mozzarella Cheese Stick Appetizers.
1.4. Datasets for panellist and panel performance evaluation.
1.5. References.
Chapter 2: Panelist and Panel Performance a Multivariate Experience.
2.1. The multivariate nature of sensory evaluation.
2.2. Univariate approaches to panelist assessment.
2.3. Multivariate techniques for panelist performance.
2.4. Panel Evaluation through Multivariate Techniques.
2.5. Conclusions.
2.6. References.
Chapter 3: A Non-Technical Description of Preference Mapping.
3.1. Introduction.
3.2. Internal preference mapping.
3.3. External Preference Mapping (PREFMAP).
3.4. Conclusions.
3.5. References.
Chapter 4: Deterministic extensions to preference mapping techniques.
4.1. Introduction.
4.2. Application and models available.
4.3. Conclusions.
4.4 References.
Chapter 5: Multidimensional scaling and unfolding and the application of probabilistic unfolding to model preference data.
5.1 Introduction.
5.2. Multidimensional Scaling (MDS) and Unfolding.
5.3. Probabilistic Approach to Unfolding and Identifying the Drivers of Liking®.
5.4. Examples.
5.5. References.
Chapter 6: Consumer Segmentation Techniques.
6.1. Introduction.
6.2. Methods Available.
6.3. Segmentation Methods using Hierarchical Cluster Analysis.
6.4. References.
Chapter 7: Ordinal Logistic Regression Models in Consumer Research.
7.1. Introduction.
7.2. Limitations of ordinary least square regression.
7.3. Odds, odds ratio and logit.
7.4. Binary logistic regression.
7.5. Multinomial logistic regression.
7.6. Ordinal logistic regression.
7.7. Conclusions.
7.8. References.
Chapter 8: Risk assessment in sensory and consumer science.
8.1. Introduction.
8.2. Concepts of Quantitative Risk Assessment.
8.3. A Case Study: Cheese Sticks Appetizers8.4. Conclusions.
8.5. References.
Chapter 9: Application of MARS to Preference Mapping.
9.1. Introduction.
9.2. MARS Basics.
9.3. Setting Control Parameters and Refining Models.
9.4. Example of application of MARS9.5. A comparison with Partial Least Squares Regression.
9.6. References.
Chapter 10: Analysis of Just About Right data.
10.1. Introduction.
10.2. Basics of Penalty Analysis.
10.3. Boot Strapping Penalty Analysis.
10.4. Use of MARS to model JAR data.
10.5. A proportional Odds/Hazards approach to diagnostic data analysis.
10.6. Use of dummy variables to model JAR data.
10.7 References.
Index