An Optimization Approach Coupling Preprocessing with Model Regression for Enhanced Chemometrics

Chemometric methods are broadly used in the chemical and biochemical sectors. Typically, derivation of a regression model follows data preprocessing in a sequential manner. Yet, preprocessing can significantly influence the regression model and eventually its predictive ability. In this work, we investigate the coupling of preprocessing and model parameter estimation by incorporating them simultaneously in an optimization step. Common model selection techniques rely almost exclusively on the performance of some accuracy metric, yet having a quantitative metric for model robustness can prolong model up-time. Our approach is applied to optimize for model accuracy and robustness. This requires the introduction of a novel mathematical definition for robustness. We test our method in a simulated set up and with industrial case studies from multivariate calibration. The results highlight the importance of both accuracy and robustness properties and illustrate the potential of the proposed optimization approach toward automating the generation of efficient chemometric models.

Automated summary

This study investigates the coupling of preprocessing and model parameter estimation, incorporating them simultaneously in an optimization step to examine their impact on regression model and its predictive ability. The authors introduce a novel mathematical definition for model robustness, optimizing for both model accuracy and robustness. The results demonstrate the importance of both accuracy and robustness properties and show the potential of the proposed optimization approach in automating the generation of efficient chemometric models.