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Multivariate Data Analysis in Sensory and Consumer Science

Multivariate Data Analysis in Sensory and Consumer Science

Garmt B. Dijksterhuis (Editor)

ISBN: 978-0-470-38505-0

Apr 2008, Wiley-Blackwell

320 pages

Description

This book is an outgrowth of research done by Dr. Gamt Dijsterhuis for his doctoral thesis at the University of Leiden. However, there are also contributions by several other authors, as well, including Eeke van der Burg, John Gower, Pieter Punter, Els van den Broek, and Margo Flipsen.

This book discusses the use of Multivariate Data Analysis to solve problems in sensory and consumer research. More specifically the focus is on the analysis of the reactions to certain characteristics of food products, which are in the form of scores given to attributes perceived in the food stimuli; the analyses are multivariate; and the senses are mainly the senses of smell and taste.

The four main themes covered in the book are: (1) Individual Differences, (2) Measurement Levels; (3) Sensory-Instrumental Relations, and (4) Time-Intensity Data Analysis.

The statistical methods discussed include Principle Components Analysis, Generalized Procrustes Analysis, Multidimensional Scaling, Redundancy Analysis, and Canonical Analysis.

This book will be a value to all professionals and students working in the sensory studies

Prologue and Acknowledgements.

Introduction..

Part I: Individual Differences.

Assessing Panel Consonance.

Interpreting Generalized Procrustes Analysis ""Analysis of Variance"" Tables..

Part II: Measurement Levels.

Multivariate Analysis of Coffee Images.

Nonlinear Canonical Correlation Analysis of Multiway Data.

Nonlinear Generalised Canonical Analysis: Introduction and Application from Sensory Research..

Part III: Sensory-Instrumental Relations.

An Application of Nonlinear Redundancy Analysis.

An Application of Nonlinear Redundancy Analysis and Canonical Correlation Analysis.

Procurstes Analysis in Studying Sensory-Instrumental Relations..

Part V: Time-Intensity Data Analysis.

Principal Component Analysis of Time-Intensity Bitterness Curves.

Principal Component Analysis of Time-Intensity Curves: Three Methods Compared.

Matching the Shape of Time-Intensity Curves.

Concluding Remarks.

References.

Abbreviations and Acronyms.

Author Index.

Subject Index.