Mutual information-induced interval selection combined with kernel partial least squares for near-infrared spectral calibration.

With the aim of developing a nonlinear tool for near-infrared spectral (NIRS) calibration, an applicable algorithm, called MIKPLS, is designed based on the combination of two different strategies, i.e. mutual information (MI) for interval selection and kernel partial least squares (KPLS) for modeling. Due to the ability of capturing linear and nonlinear dependencies between variables simultaneously, mutual information between each candidate variables and target is calculated and employed to induce a continuous wavelength interval, which is subsequently applied to build a parsimonious calibration model for future use by kernel partial least squares. Through the experiments on two datasets, it seems that mutual information (MI)-induced interval selection, followed by KPLS, forms a very simple and practical tool, allowing a prediction model to be constructed using a much-reduced set of neighboring variables, but without any loss of generalizations and with improved prediction performance instead.