Optimization of Partial-Least-Squares Calibration Models by Simulation of Instrumental Perturbations

Abstract

A critical step in partial-leasts-squares (PLS) modeling is the model optimization. Cross-validation is often applied, but in spite of its statistical properties, it suffers some severe shortcomings. In particular, cross-validation has a tendency to give overfitted models, whereas parsimonious models should be preferred. We propose an alternative form of internal validation, based on the simulation of instrumental perturbations on a subset of calibration samples. A simple criterion is proposed for the adjustment of perturbations. The method is applied for the validation of nine PLS1 calibration models on industrial data sets and compared with cross-validation and cross-validation combined with a randomization test. It is shown that parsimonious models can be obtained, with a good predictive power when they are applied to external test data.