A simple ensemble strategy of uninformative variable elimination and partial least-squares for near-infrared spectroscopic calibration of pharmaceutical products

Abstract Based on uninformative variable elimination (UVE) and partial least squares (PLS), a simple ensemble strategy, named EUVEPLS, is proposed for multivariate calibration of near-infrared spectroscopy. In such a strategy, different calibration sets are first formed by randomly selecting a fixed number of objects from the available calibration data, with replacement, in this manner, different PLS models can be obtained, as in the jackknife procedure. Then, the UVE is used to shrink the original variable space into a specific subspace, in which a PLS model with the optimal number of latent variables is determined by cross-validation. This process is repeated until a fixed number of candidate models, and therefore the same number of subspaces, are obtained. Finally, those models with better performance are used to produce an ensemble model by simple/weighted average. Two NIR spectral datasets concerning the pharmaceutical products are used to verify the accuracy and robustness of the proposed EUVEPLS. The results confirmed the superiority of EUVEPLS to two single model reference methods (UVEPLS and full-spectrum PLS).