Stability competitive adaptive reweighted sampling (SCARS) and its applications to multivariate calibration of NIR spectra

Abstract

A new variable selection method called stability competitive adaptive reweighted sampling (SCARS) is proposed based on competitive adaptive reweighted sampling (CARS). In SCARS, variable is selected by an index of stability that is defined as the absolute value of regression coefficient divided by its standard deviation. SCARS algorithm consists of a number of loops. In each loop, the stability of each variable is computed. Then based on stability, enforced wavelength selection and adaptive reweighted sampling (ARS) is used to select important variables. The selected variables are kept as a variable subset and further used in the next loop. After running the loops, a number of subsets of variables are obtained and root mean squared error of cross validation (RMSECV) of PLS models established with subsets of variables is computed. The subset of variables with the lowest RMSECV is considered as the optimal variable subset. The performance of the proposed algorithm is evaluated by three near-infrared (NIR) datasets: tobacco, corn and glucose datasets. The results show that SCARS can select the least variables and supply the least RMSECV and latent variable number of the PLS model comparing with methods of Moving Window PLS (MWPLS), MCUVE and CARS.