Received 31 October 2010 revised 23 March 2011 accepted 2 April 2011 Available online Īrtificial neural network Multiple regression analysis Method developmentĪbstract This paper discusses the usefulness of artificial neural networks (ANNs) for response surface modeling in HPLC method development. Maherĭepartment of Pharmaceutical Analytical Chemistry, Faculty of Pharmacy, University of Alexandria, Alexandria 21521, Egypt Journal of Advanced Research (2012) 3, 53-63Īpplication of artificial neural networks for response surface modeling in HPLC method development It was clear that the best networks were able to predict the experimental responses more accurately than the multiple regression analysis. %), and coefficients of correlation (r) were compared. To examine the respective predictive power of the regression model and the neural network model, experimental and predicted response factor values, mean of squares error (MSE), average error percentage (Er The results were compared with those produced using multiple regression (REG) analysis. In this study, the combined effect of pH and mobile phase composition on the reversed-phase liquid chromatographic behaviour of a mixture of salbutamol (SAL) and guaiphenesin (GUA), combination I, and a mixture of ascorbic acid (ASC), paracetamol (PAR) and guaiphenesin (GUA), combination II, was investigated.

This paper discusses the usefulness of artificial neural networks (ANNs) for response surface modelling in HPLC method development. Abstract of research paper on Chemical sciences, author of scientific article - Mohamed A.

Statistica neural networks 6.1.