Sensory analysis via multi-block multivariate additive PLS splines

In the last decade, much effort has been spent on modelling dependence between sensory variables and chemical–physical ones, especially when observed at different occasions/spaces/times or if collected from several groups (blocks) of variables. In this paper, we propose a nonlinear generalization of multi-block partial least squares with the inclusion of variable interactions. We show the performance of the method on a known data set.