OPLS discriminant analysis: combining the strengths of PLS‐DA and SIMCA classification

Abstract The characteristics of the OPLS method have been investigated for the purpose of discriminant analysis (OPLS‐DA). We demonstrate how class‐orthogonal variation can be exploited to augment classification performance in cases where the individual classes exhibit divergence in within‐class variation, in analogy with soft independent modelling of class analogy (SIMCA) classification. The prediction results will be largely equivalent to traditional supervised classification using PLS‐DA if no such variation is present in the classes. A discriminatory strategy is thus outlined, combining the strengths of PLS‐DA and SIMCA classification within the framework of the OPLS‐DA method. Furthermore, resampling…