Stability indicating analysis of bisacodyl by partial least squares regression, spectral residual augmented classical least squares and support vector regression chemometric models: A comparative study

Partial least squares regression (PLSR), spectral residual augmented classical least squares (SRACLS) and support vector regression (SVR) are three different chemometric models. These models are subjected to a comparative study that highlights their inherent characteristics via applying them to analysis of bisacodyl in the presence of its reported degradation products monoacetyl bisacodyl (I) and desacetyl bisacodyl (II), in raw material. For proper analysis, a 3 factor 3 level experimental design was established resulting in a training set of 9 mixtures containing different ratios of the interfering species. A linear test set consisting of 6 mixtures was used to validate the prediction ability of the suggested models. To test the generalisation ability of the models, some extra mixtures were prepared that are outside the concentration space of the training set. To test the ability of models to handle nonlinearity in spectral response, another set of nonlinear samples was prepared. The paper highlights model transfer to other labs under other conditions as well. This paper aims to manifest the advantages of SRACLS and SVR over PLSR model, where SRACLS can tackle future changes without the need for tedious recalibration, while SVR is a more robust and general model, with high ability to model nonlinearity in spectral response, though like PLSR is needing recalibration. The results presented indicate the ability of the three models to analyse bisacodyl in the presence of its degradation products in raw material with high accuracy and precision; where SVR gives the best results at all tested conditions compared to other models.