Factor selection strategies for orthogonal signal correction applied to calibration of near-infrared spectra

Orthogonal signal correction (OSC) is a preprocessing technique used for correction of instrumental drift, bias and scatter in near-infrared spectra. OSC separates the variation into orthogonal factors, where the factors contain the variation within the X matrix data that is not correlated with the y matrix vector data. The aim of this study is to investigate different orthogonal factor selection methods, which will enhance the performance of the OSC routine for quantitative analysis of near-infrared spectra. In order for factor selection methods to be applied to OSC, an implementation of an existing OSC algorithm is used; this method computes OSC factors in a principal component manner. A binarized weighting matrix is then applied to the OSC factors for the purpose of OSC factor subset selection/nonselection. The optimization strategies used for subset selection of OSC factors were a genetic algorithm and stepwise selection. Three data sets were formed: (1) no preprocessing, (2) preprocessing by removal of sequential OSC factors and (3) preprocessing by removal of an OSC factor subset. Combinations of spectral predictors were selected from these data sets by hill climbing, feature selection, genetic algorithm and full-spectrum modeling. Partial least squares regression was undertaken to form calibration models. It was found that selection of OSC factor subsets produced better standard errors of prediction relative to data preprocessed by sequential selection of OSC factors.