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Simultaneous spectrophotometric determination of chlordiazepoxide and clidinium using multivariate calibration techniques
Author(s) -
Khoshayand Mohammad Reza,
Abdollahi Hamid,
Moeini Ali,
Shamsaie Ali,
Ghaffari Alireza,
Abbasian Sepideh
Publication year - 2010
Publication title -
drug testing and analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.065
H-Index - 54
eISSN - 1942-7611
pISSN - 1942-7603
DOI - 10.1002/dta.162
Subject(s) - partial least squares regression , chlordiazepoxide , multivariate statistics , calibration , artificial neural network , mean squared prediction error , principal component regression , principal component analysis , regression , standard error , computer science , mathematics , statistics , chromatography , chemistry , artificial intelligence , biology , pharmacology , diazepam
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. Copyright © 2010 John Wiley & Sons, Ltd.