|Dijksterhuis, Garmt B. (Hrsg.)|
Multivariate Data Analysis in Sensory and Consumer Science
1. Auflage Juli 2008
2008. 320 Seiten, Hardcover
ISBN 978-0-917678-41-7 - John Wiley & Sons
Preis inkl. Mehrwertsteuer zzgl. Versandkosten.
This book is an outgrowth of research done by Dr. Gamt Dijsterhuisfor his doctoral thesis at the University of Leiden. However, thereare also contributions by several other authors, as well, includingEeke van der Burg, John Gower, Pieter Punter, Els van den Broek,and Margo Flipsen.
This book discusses the use of Multivariate Data Analysis tosolve problems in sensory and consumer research. More specificallythe focus is on the analysis of the reactions to certaincharacteristics of food products, which are in the form of scoresgiven to attributes perceived in the food stimuli; the analyses aremultivariate; and the senses are mainly the senses of smell andtaste.
The four main themes covered in the book are: (1) IndividualDifferences, (2) Measurement Levels; (3) Sensory-InstrumentalRelations, and (4) Time-Intensity Data Analysis.
The statistical methods discussed include Principle ComponentsAnalysis, Generalized Procrustes Analysis, MultidimensionalScaling, Redundancy Analysis, and Canonical Analysis.
This book will be a value to all professionals and studentsworking in the sensory studies
Aus dem Inhalt
Prologue and Acknowledgements.
Part I: Individual Differences.
Assessing Panel Consonance.
Interpreting Generalized Procrustes Analysis "Analysis ofVariance" Tables..
Part II: Measurement Levels.
Multivariate Analysis of Coffee Images.
Nonlinear Canonical Correlation Analysis of Multiway Data.
Nonlinear Generalised Canonical Analysis: Introduction andApplication from Sensory Research..
Part III: Sensory-Instrumental Relations.
An Application of Nonlinear Redundancy Analysis.
An Application of Nonlinear Redundancy Analysis and CanonicalCorrelation Analysis.
Procurstes Analysis in Studying Sensory-InstrumentalRelations..
Part V: Time-Intensity Data Analysis.
Principal Component Analysis of Time-Intensity BitternessCurves.
Principal Component Analysis of Time-Intensity Curves: ThreeMethods Compared.
Matching the Shape of Time-Intensity Curves.
Abbreviations and Acronyms.