Multiblock partial least squares regression based on wavelet transform for quantitative analysis of near infrared spectra

Abstract

Multiblock partial least squares (MB-PLS) method has been proposed for modeling the data set with large number of variables and for making the model more interpretable. In MB-PLS, the variables are split into several blocks containing different information, and the relative importance of the blocks is reflected by the super-weights of the MB-PLS model. In this paper, a weighted MB-PLS coupled with discrete wavelet transform (DWT) method is proposed for modeling of the near infrared (NIR) spectra. In the method, the spectra are decomposed into blocks by DWT, and the relative importance of the blocks is estimated by both the super-weights and the block-weights determined by the prediction error of the sub-models in cross validation. Therefore, a practical approach to separate the variables is provided for MB-PLS and the relative contribution of the variable blocks to the prediction can be modulated adaptively. To validate the performance of the method, two industrial NIR data sets of tobacco powder and fragment of tobacco lamina are investigated, respectively. The root-mean-square error of prediction (RMSEP), the residual predictive deviation (RPD), and the correlation coefficient ( R) show that the weighted MB-PLS coupled with DWT gives a better predictive accuracy and interpretability compared with the ordinary PLS and MB-PLS methods.