Improved kernel PLS combined with wavelength variable importance for near infrared spectral analysis

•The wavelength variable importance is incorporated into KPLS by modifying kernel matrix based on weighted bootstrap sampling.•VIKPLS provides a feasible way to differentiate between the informative and uninformative variables.•The performance on three real NIR datasets shows our method can improve the prediction ability of KPLS.•VIKPLS could be considered as a general mechanism to introduce extra information to improve the performance of KPLS.

In this study, a new strategy called variable importance kernel PLS (VIKPLS) method is developed for near infrared spectral analysis. The wavelength variable importance is incorporated into KPLS by modifying the primary kernel matrix, and variables in the kernel matrix are given the different importance, which provides a feasible way to differentiate between the informative and uninformative variables. The importance of variables is determined by the frequency of variables appearing in the best performing sub-models based on the weighted bootstrap sampling. The performance of VIKPLS is investigated with three real near infrared(NIR) spectroscopic datasets. Examples are given specifically for modifying the linear kernel and Gaussian kernel. Compared with standard kernel PLS, the results show the proposed method can improve the training and prediction performance of KPLS by using variable importance kernel. VIKPLS could be considered as a general and promising mechanism to introduce extra information to improve the performance of KPLS.