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 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. (C) 1997 Elsevier Science Ltd. All rights reserved.