Approaches to data mining reaction kinetics using Raman spectroscopic analysis of Claisen-Schmidt condensation as a model

Raman spectroscopy provides a powerful, non-destructive tool for real-time monitoring of multicomponent kinetics. However, extraction of reliable kinetic information from kinetically evolving Raman data can be challenging, due to the degree of spectral overlap of constituent signatures, rank deficiency, and rotational ambiguity. In this work, as a model reaction, solvent-free, base-catalysed Claisen–Schmidt condensation between benzaldehyde and acetone at room temperature was monitored using Raman microspectroscopy, in-situ, over a timescale of 1000 min. The resulting multicomponent dataset was analysed using problem-based nonlinear least squares (NLS) fitting of the weighted sums of the spectra of the reaction components, and multivariate curve resolution-alternating least squares (MCR-ALS) analysis. Three and four-components models were explored within the NLS framework, while a mechanistically constrained model (A + B → C, C + A → D) was implemented for MCR-ALS hard modelling. Conventional soft and hard MCR-ALS approaches failed to identify the correct reaction components and resolve chemically meaningful profiles, whereas seeded MCR-ALS, with 10,000× optimized seed weightings, successfully overcame these failings and yielded physically consistent spectra and concentration profiles. Reaction kinetics were evaluated by fitting the resolved concentration profiles to a kinetic model (A → C → D). The seeded MCR-ALS approach provided the most reliable kinetic description, while, in comparison, NLS based approaches using resolved and pure components spectra exhibited lower fitting accuracy. Quantitatively, the seeded MCR-ALS model showed superior performance, co-efficient of determination (R 2 ) = 0.935, residual sum of squares (RSS) = 0.156, relative residual error (RRE) = 0.70% relative to NLS using resolved spectra (R 2 = 0.782, RSS = 0.524, RRE = 2.34%) and NLS using pure spectra (R 2 = 0.783, RSS = 0.408, RRE = 2.33%). These results demonstrate that seeded MCR-ALS, combined with constrained exponential kinetic modelling provides a robust and physically consistent framework for resolving spectroscopic data and extracting reliable mechanistic and kinetic insights. The proposed methodology is broadly applicable to complex multicomponent systems, including metabolomics, process analytics, and pharma kinetics, in which severe spectral overlap and rank deficiency limit conventional analytical approaches.