Using Compact 1H NMR, NIR, and Raman Spectroscopy Combined with Multivariate Data Analysis to Monitor a Biocatalyzed Reaction in a Microreaction System

Abstract

Process analytical technology aims at process knowledge and process improvement, efficiency, and sustainability. A prerequisite is process monitoring. The combination of microreaction systems and spectroscopy proved suitable due to dimension and compound reduction and real-time monitoring capabilities. Compact 1H NMR, NIR, and Raman spectroscopy were used to monitor the biocatalyzed hydrolysis and esterification of acetic anhydride to isoamyl acetate using immobilized Candida antarctica lipase B (CALB) in a microreaction system in real-time. To facilitate the identification of signals suitable for the extraction of concentration-time (c-t) graphs, 2D heterocorrelation spectra were generated through covariance transformations applied to 1D Raman, NIR, and NMR data. By means of this purely mathematical statistical procedure, the relevant signals of the process media were assigned to educts and products and thus made applicable for univariate data evaluation. The data obtained were interpreted in terms of a first-order kinetic model, and corresponding reaction rate constants were extracted. An alternative, elegant, and fit-for-automation approach for the kinetic analysis of the spectra was demonstrated in using multivariate curve resolution (MCR). The results of the univariate and multivariate approaches were comparable with regard to reaction rates and concentrations. While the manual integration of the 1H NMR spectra followed by univariate analysis allowed to establish a concentration profile of the final product isoamyl acetate hence revealing more details, multivariate analysis was found more suitable for process automation.