Raman spectroscopic monitoring of multiproduct chemical reaction kinetics; the case of ester hydrolysis

Abstract

Raman Spectroscopy (RS) is a powerful technique for the identification of molecules based on the characteristic fingerprint spectra of their vibrational modes. Although challenging, real time spectroscopic monitoring of reactions and processes has great potential value in multiple fields, including process analysis, bio reactors, cell therapies and in vitro metabolomics. Refined chemometrics methodologies are required to datamine the kinetic evolution of multivariate spectral mixtures to establish the constituent reactants and products, as well as the characteristic rates of the reaction. To explore the capabilities and challenges, RS was used to study the chemical kinetics of propyl acetate hydrolysis in an aqueous environment at room temperature, in situ, as a model reaction. The continuous conversion of propyl acetate to 1-propanol and acetic acid was monitored periodically over 250 min using RS with a 532 nm laser source. Simulated admixture solutions, mimicking the reaction from pure reactants to pure products conversion, were also recorded for comparison. Problem based nonlinear least squares (NLS) fitting was applied to both the actual reaction and simulated solution data sets using pure components spectra of both the reactants, propyl acetate, water and products, 1-propanol and acetic acid, in order to visualise and confirm the trends and kinetics of the reaction components. Multivariate Curve Resolution-Alternating Least Squares analysis (MCR-ALS) with kinetic constraints was applied to further resolve the concentration and spectral profiles and to quantify the rates of the reaction. It is demonstrated that MCR-ALS could not accurately resolve the evolving reaction species with respect to concentration, due to rank deficiency. To enhance the analysis, a data augmentation approach was used, seeding the measured datasets with the spectra of the pure components to bias the initial singular value decomposition and spectral unmixing process, resulting in an improved resolution of the systematic variation of concentration dependent data to monitor the kinetic evolution of the reaction mixture. The required seeding weights were optimized by visualizing the sum residual error (SUMR) in least squares fitting of the actual components with the identified pure components by MCR-ALS. Minimum SUMR values for admixtures were found at a seeding weight of 10000X, while 100X was found to be optimum for the actual reaction. This proof of concept can further pave the way for better analysis and understanding of cascade reactions, and ultimately, potentially of metabolomic pathways.