Chemometric methods for the online monitoring of pharmaceutical reactions using near-infrared spectroscopy in a multi-reactor system

The combination of spectroscopy and multivariate statistical modeling methods constitutes a powerful tool, with applications in process monitoring, modeling, and optimization. This thesis establishes how near-infrared spectroscopy utilizing fiber optic transmission probes can be coupled with a robotic multi-reactor system to obtain reaction spectra online from several reactors simultaneously. Multivariate calibration tools can then be implemented to model the spectra and estimate component concentration profiles. It is demonstrated that near-infrared transmission spectroscopy provides a quick and accurate method of measuring liquid-phase composition.; This methodology is based on a calibration developed by sampling aliquots of an aspirin hydrolysis reaction for off-line HPLC reference analysis immediately after acquiring a near-infrared spectrum. Calibration models were developed using partial least squares (PLS-1 and PLS-2) methodology via the leave-one-out cross-validation technique for factor selection. Optimal models were selected based upon the minimization of relative standard error through prediction of independent validation reactions. The selected models could accurately quantify aspirin and salicylic acid reaction concentrations in real-time with less than 6% error. An extension of this project is to use the developed models and instrumentation as a means to estimate reaction stoichiometry and kinetics both off-line and on-line through tendency modeling methodology.