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Simultaneous estimation of all drug components in a multicomponent an- algesic dosage form with artificial neural networks calibration models using UV spectrophotometry is reported as a simple alternative to using separate models for each component. A novel approach for calibration using a compund spectral dataset derived from three spectra of each component is described. The spectra of mefe- namic acid and paracetamol were recorded as several concentrations within their linear range and used to compute a calibration mixture between the wavelengths 220 to 340 nm. Neural networks trained by a Levenberg-Marquardt algorithm were used for building and optimizing the calibration models using MATALAB ® Neural Net- work Toolbox and were compared with the principal component regression model. The calibration models were throughly evaluated at several concentration levels us- ing 104 spectra obtained for 52 synthetic binary mixtures prepared using orthogonal designs. The optimized model showed sufficient robustness even when the calibra- tion sets were constructed from a different set of pure spectra of the components. The simultaneous prediction of both components by a single neural netwook with the suggested calibration approach was successful. The model could accurately esti- mate the drugs, with satisfactory precision and accuracy, in tablet dosage with no in- terference from excipients as indicated by the results of a recovery study.

journal of the serbian chemical society

Principal component articial neural network calibration models for the simultaneous spectrophotometric estimation of mefenamic acid and paracetamol in tablets