Comparison between linear and nonlinear PLS methods to explain overall liking from sensory characteristics

The influence of sensory characteristics on overall liking can be statistically studied with Partial Least Squares (PLS) regression methods. To correctly model nonlinear dependence relationships, some nonlinear PLS extensions are useful. The purpose of the present paper is to compare performances an...

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Bibliographic Details
Published inFood quality and preference Vol. 8; no. 5; pp. 395 - 402
Main Authors de Kermadec, F.Huon, Durand, J.F., Sabatier, R.
Format Journal Article Conference Proceeding
LanguageEnglish
Published Oxford Elsevier Ltd 1997
Elsevier
Subjects
Biological and medical sciences
chocolate
Food industries
Fundamental and applied biological sciences. Psychology
General aspects
Methods of analysis, processing and quality control, regulation, standards
principal component analysis
regression analysis
sensory evaluation
PLS regression
Chocolate confectionery
Non linear regression
Linear regression
Model study
Sensory analysis
Foodstuff
Feeding preference
Modeling
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Summary:The influence of sensory characteristics on overall liking can be statistically studied with Partial Least Squares (PLS) regression methods. To correctly model nonlinear dependence relationships, some nonlinear PLS extensions are useful. The purpose of the present paper is to compare performances and results of three PLS methods, using a real data set: regular PLS with sensory attributes as explanatory variables; PLS with attributes and their respective squares; and a new nonlinear PLS extension, called ASPLS. In case of a nonlinear dependence relationship between sensory characteristics and hedonic responses, this last method is shown to be worth considering.
ISSN:0950-3293
1873-6343
DOI:10.1016/S0950-3293(97)00026-8