Variable space boosting partial least squares for multivariate calibration of near-infrared spectroscopy

Abstract

A novel boosting strategy by establishing sub-model from variable direction named variable space boosting partial least squares (VS-BPLS) was proposed for multivariate calibration of near-infrared (NIR) spectroscopy. At the first cycle, all the variables in the training set are given the same sampling weights and then a certain number of variables are selected to build PLS sub-model according to the distribution of the sampling weights. In the following cycles, the sampling weights of the variables in the training set are given by a predefined loss function. This loss function is about the error of known and predicted spectra that is obtained by the product of score and loading of PLS sub-models. The final prediction for unknown sample is obtained by the weighted average of each prediction of all the sub-models. The proposed method not only can solve the small sample problem, but also remove redundant information in variables. The performance of VS-BPLS is tested with two NIR spectral datasets. As comparisons to VS-BPLS, the conventional PLS and two variable selection method Monte Carlo uninformative variable elimination PLS (MCUVE-PLS) and randomization test PLS (RT-PLS) have also been investigated. Results show that VS-BPLS has a superiority for small sample problems in prediction accuracy and stability compared with the PLS, MCUVE-PLS and RT-PLS.