AbstractA generic preprocessing method for multivariate data, called orthogonal projections to latent structures (O‐PLS), is described. O‐PLS removes variation from X (descriptor variables) that is not correlated to Y (property variables, e.g. yield, cost or toxicity). In mathematical terms this is equivalent to removing systematic variation in X that is orthogonal to Y. In an earlier paper, Wold et al. (Chemometrics Intell. Lab. Syst. 1998; 44: 175–185) described orthogonal signal correction (OSC). In this paper a method with the same objective but with different means is described. The proposed O‐PLS method analyzes the variation explained in each PLS component. The non‐correlated systematic variation in X is removed, making interpretation of the resulting PLS model easier and with the additional benefit that the non‐correlated variation itself can be analyzed further. As an example, near‐infrared (NIR) reflectance spectra of wood chips were analyzed. Applying O‐PLS resulted in reduced model complexity with preserved prediction ability, effective removal of non‐correlated variation in X and, not least, improved interpretational ability of both correlated and non‐correlated variation in the NIR spectra. Copyright © 2002 John Wiley & Sons, Ltd.
