Abstract
This chapter describes a strategy based on orthogonal projections to latent structures (OPLS) methodology for evaluation of different preprocessing techniques applied to spectroscopic data. Preprocessing multivariate data always involves a risk of removing variation, which contains information that is related to the problem at hand. The O2PLS (bidirectional OPLS) modeling provides an easy overview of the original data in comparison to the different preprocessed data sets. The model-based analysis of the different types and amounts of variation associated with each type of preprocessing method provides the user with a number of options, for example, to verify the desired effect of pretreatment. It is also possible to diagnose the unwanted variation and thus establish a good knowledge base for deciding the appropriate method of pretreatment. Ultimately, applying OPLS methodology before multivariate modeling will aid in deciding whether preprocessed data should be used and, if so, which method should be applied. The approach is general and should be applicable to other types of data.
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