In a previous tutorial here at Chemometrics.se, I discussed why Partial Least Squares Projections to Latent Structures (PLS) sometimes needs more PLS components than Y-variables. It was shown that this was due to strong systematic but irrelevant (Y-orthogonal) variation in X. It was also shown that if PLS has more than one component / Y variable, the interpretation (not prediction) of the PLS model suffers in direct relation to the additional number of PLS components needed.

In this tutorial, I want to describe a new set of pre-processing methods that can be used to remove the systematic Y-orthogonal variation from X. These methods are the Orthogonal Signal Correction (OSC) filters. I will describe what they are, why and when they are useful. As a cliffhanger to get your attention for next month's editorial, I will also explain why these OSC filters are not optimal for the two-block (X-Y) situation, as one might expect.

Download this tutorial (pdf)