Quadratic PLS regression applied to external preference mapping

• Quadratic PLS regression is outlined and extended to the multivariate setting.
• Its interest in external preference mapping is highlighted.
• An illustration and a comparison of methods are discussed.

In sensory analysis, preference mapping covers several modeling techniques applied to hedonic data for a better understanding of consumers’ liking and product optimization. External preference mapping aims at relating consumers’ hedonic data to sensory data in order to identify liking drivers. Classically, preference mapping proceeds in two steps: the first step consists in defining the perceptual space on which preference data are regressed and, the second step, identifies the predictive model according to this perceptual space. The strategy of analysis (quadratic PLS regression) presented herein fits within the framework of PLS regression. An optimal perceptual space is sought by taking account of the linear and the quadratic relationships between hedonic and sensory data. Quadratic PLS is compared to other methods of analysis on the basis of a case study related to coffee data.