Interactive variable selection (IVS) for PLS. Part II: Chemical applications

With the aim of developing PLS models with improved predictive properties, an interactive variable selection (IVS) approach for PLS regression was introduced in Part I of this series. IVS‐PLS is based on a dimension‐wise selective removal of single elements in the PLS weight vector w. IVS uses cross‐validation (CV) as a guiding tool. The present paper illustrates the use of IVS‐PLS on both simulated data and real examples from chemistry. In the first example, spectrophotometric data were simulated according to an experimental design. The objective was to see how IVS‐PLS was influenced by different levels of noise in X and Y and by the number of predictor variables (K). The results of the modelling are shown as response surfaces. In addition, four real examples were modelled by the IVS‐PLS technique. The real data sets were chosen to reflect different types of data from chemistry. For each example a comparison of ‘prediction error sum of squares’ (PRESS) between IVS‐PLS and classical PLS is made For most of the examples containing many predictor variables IVS‐PLS shows an improvement in predictive properties over classical PLS. Also, improvements for IVS‐PLS2 (modelling of more than one y‐variable) models were found. For data sets with a moderate number of variables the influence of the IVS method becomes less pronounced.