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ABSTRACT: Three multivariate modelling approaches including partial least squares regression (PLS), genetic algorithm-partial least squares regression (GA-PLS), and principal components-artificial neural network (PC-ANN) analysis were investigated for their application to the simultaneous determination of chlordiazepoxide and clidinium levels in pharmaceuticals. A set of synthetic mixtures of drugs in ethanol and 0.1 M HCL was made, and the prediction abilities of the aforementioned methods were examined using RSE% (relative standard error of the prediction). The PLS and PC-ANN methods were found to be comparable, and GA-PLS produced slightly better results. The predictive models that we built were successfully applied to simultaneously determine the levels of chlordiazepoxide and clidinium in coated tablets.
Drug Testing and Analysis 09/2010; 2(9):430-5. DOI:10.1002/dta.162 · 2.82 Impact Factor