Separating Y-predictive and Y-orthogonal variation in multi-block spectral data

Spectral data (X) may contain (a) variation that is correlated to concentrations or properties (Y) of samples and (b) variation that is unrelated to the same Y. This paper outlines an approach by which both such sources of variation may be resolved. The approach is based on a combination of hierarchical modelling and orthogonal partial least squares (OPLS). OPLS is first used at the base hierarchical level. The output is a labelling of the resulting score vectors as representing Y-predictive or Y-orthogonal variation. OPLS is then also used at the top hierarchical level together with principal components analysis (PCA). With PCA the Y-orthogonal X-variation is analysed and interpreted. With OPLS the Y-predictive X-variation is examined. The applicability of the proposed strategy is illustrated using one multi-block spectral data set. Copyright (c) 2007 John Wiley & Sons, Ltd.