Comparison of multivariate methods based on latent vectors and methods based on wavelength selection for the analysis of near-infrared spectroscopic data

Comparison of several calibration methods (principal component regression (PCR), partial least-squares, multiple linear regression), with and without feature selection, applied on near-infrared spectroscopic data is presented for a pharmaceutical application. It is shown that PCR with selection of principal components instead of the usual top-down approach yields simpler and better models. As feature selection methods, selection of wavelengths correlated with concentration, with large covariance with concentration, with high loadings on the important principal components, and according to a method proposed by Brown, are considered. The presented results suggests that feature selection can improve multivariate calibration.