Resolution and quantification challenge of modern chemometric models in the determination of anti-migraine tablets containing ergotamine, caffeine, acetaminophen, and metoclopramide

Abstract

This study is a comparison between the performance of five multivariate models in the determination of the unique mixture of ergotamine (ERG), metoclopramide (MET), caffeine (CAF), and paracetamol (PAR) in laboratory-prepared mixtures and in pharmaceutical formulations. Two supervised learning machine methods—artificial neural networks (PC-ANN) preceded by principle component analysis and support vector regression (SVR)—were compared with a spectral residual augmented classical least squares (SRACLS) method, multicurve resolution alternating least squares (MCR-ALS) method, and principle component based method; partial least squares (PLS). The results showed the superiority of linear learning machine methods in handling extremely noisy and complex spectral data, especially during the determination of the challenging mixture under study. ERG (the component with a close to undetectable concentration and with the lowest ratio in the studied dosage form) was only determined using three chemometric models, with root mean squared error of prediction (RMSEP) for the proposed models of 0.0879, 0.0694, and 0.0250 for PLS, SVR and PC-ANN, respectively. In addition, the results suggest that ANN is the method of choice for the determination of mixtures with extreme conditions; for example, components with a very low contribution in the overall spectra, components with narrow informative range, and extremely nonlinear spectral data.