Robustness of models developed by multivariate calibration. Part I

Abstract

Monitoring products for quality assurance in real-time during industrial processes has become of great importance in recent years. Infrared spectroscopic (IRS) techniques combined with multivariate calibration methods are primarily used for on-line analysis, in situ sensors or automatic sampling. In order to ensure the correct use of these methods for routine industrial use, all the mechanical and the environmental conditions need to be taken into account, as well as the introduction of time delays and signal bias during sampling. This requires a robustness study of the IRS measurement and the calibration model used. In this review, we focus on both identifying the “robustness” used for multivariate calibration and the different methods applied to evaluate this robustness, especially with regard to the IRS technique used in industry. We also present and discuss various criteria intended for robustness assessment.