Some recent developments in PLS modeling

Abstract

The original chemometrics partial least squares (PLS) model with two blocks of variables ( X and Y), linearly related to each other, has had several enhancements/extensions since the beginning of 1980. We here discuss multi-block and hierarchical PLS modeling for installing a priori knowledge of the data structure and simplifying the model interpretation, variable selection schemes for PLS with often similar objectives, nonlinear PLS, and prefiltered PLS, orthogonal signal correction (OSC). A very recent development, orthogonalized-PLS (O-PLS) is included as a way to accomplish both OSC, and a simpler interpretation of the PLS model. In this context, we also briefly mention time series, batch, and wavelets variants of PLS. These PLS extensions are illustrated by examples from peptide quantitative structure–activity relationships (QSAR) and multivariate characterization of pulp using NIR.