Moving window smoothing on the ensemble of competitive adaptive reweighted sampling algorithm.

A novel chemometrical method, named as MWS-ECARS, which is based on using the moving window smoothing upon an ensemble of competitive adaptive reweighted sampling, is proposed as the spectral variable selection approach for multivariate calibration in this study. In terms of elimination of uninformative variables, an ensemble of CARS is carried out first and MWS is then performed to search for effective variables around the high frequency variables. The variable subset with the lowest standard error of cross-validation (SECV) is treated as the optimal threshold and the corresponding moving window width is regarded as the optimal window width. The method was applied to mid-infrared (MIR) spectra of active ingredient in pesticide, near-infrared (NIR) spectra of soil organic matter and NIR spectra of total nitrogen in Solanaceae plants for variable selection. Overall results show that MWS-ECARS is a promising selection method with an improved prediction performance over three variable selection methods of variable importance projection (VIP), uninformative variables elimination (UVE) and genetic algorithms (GA).