Improving the Univariate Calibration Approach with Bayesian Modeling for IR Reaction Monitoring

Abstract

Infrared (IR) spectroscopy is a powerful tool for real-time reaction monitoring in chemical synthesis. For applications that require tracking concentration profiles of reactive species, univariate linear calibration models are commonly used to relate IR signals to analyte concentrations. Despite their simplicity, the accuracy of these models can be limited by spectral overlap and other effects that distort the linear relationship between concentration and signal. To address this limitation, chemometric methods are often employed, typically without further examination of opportunities to improve univariate calibration performance itself. Here, we present a novel workflow based on Bayesian statistics to enhance univariate calibration for IR reaction monitoring. The central feature of this workflow is the use of three diagnostic Bayesian probabilistic models, combined with data-preprocessing selection, to screen for IR signals that can potentially improve univariate calibration performance when non-linear effects are present. We applied the workflow to a test reaction system and identified an IR signal in the fingerprint region, along with an uncommon preprocessing strategy, that reduced prediction error by more than 50% compared with the univariate model using the original preprocessing steps. Overall, our workflow aims to improve the usability of univariate calibration approaches and expand the toolbox available to chemists for IR monitoring of complex chemical processes.