Wavelength selection method for multicomponent spectrophotometric determinations using partial least squares

Abstract

A method for eliminating unnecessary wavelengths is applied with the goal of achieving improved prediction ability in multicomponent determinations by UV/VIS spectrophotometry with partial least squares (PLS). The feature selection method is based on the regression coefficients of the closed form of the PLS model. This method was evaluated with calibration data of different types, and with different criteria to choose the optimum number of factors. The results presented suggest that wavelength selection improves the prediction ability of PLS method.