Weighted partial least squares regression by variable grouping strategy for multivariate calibration of near infrared spectra

Abstract

A weighted partial least squares (PLS) regression method for multivariate calibration of near infrared (NIR) spectra is proposed. In the method, the spectra are split into groups of variables according to the statistic values of variables, i.e., the stability, which has been used to evaluate the importance of variables in a calibration model. Because the stability reflects the relative importance of the variables for modeling, these groups present different spectral information for construction of PLS models. Therefore, if a weight which is proportional to the stability is assigned to each sub-model built with different group variables, a combined model can be built by a weighted combination of the sub-models. This method is different from the commonly used variable selection strategies, making full use of the variables according to their importance, instead of only the important ones. To validate the performance of the proposed method, it was applied to two different NIR spectral data sets. Results show that the proposed method can effectively utilize all variables in the spectra and enhance the prediction ability of the PLS model.