Genetic algorithm optimized neural networks ensemble for estimation of mefenamic acid and paracetamol in tablets

Improvements in neural network calibration models by a novel approach using neural network ensemble (NNE) for the simultaneous Spectrophotometric multicomponent analysis is suggested, with a study on the estimation of the components of an analgesic combination, namely, Mefenamic acid and Paracetamol. Several principal component neural networks were trained with the Levenberg-Marquardt algorithm by varying conditions such as inputs, hidden neurons, initialization and training sets. Genetic algorithm (GA) has been used to develop the NNE from the trained pool of neural networks. Subsets of neural networks selected from the pool by decoding the chromosomes were combined to form an ensemble. Several such ensembles formed the population which was evolved to generate the fittest ensemble. Ensembling the networks was done with weighted average decided on the basis of the mean square error of the individual nets on the validation data while the ensemble fitness in the GA optimization was based on the relative prediction error on unseen data. The use of computed calibration spectral dataset derived from three spectra of each component has been described. The calibration models were thoroughly evaluated at several concentration levels using 104 spectra obtained for 52 synthetic binary mixtures prepared using orthogonal designs. The Ensemble models showed better generalization and performance compared to any of individual neural networks trained. Although the components showed significant spectral overlap, the model could accurately estimate the drugs, with satisfactory precision and accuracy, in tablet dosage with no interference from excipients as indicated by the recovery study results. The GA optimization guarantees the selection of best combination of neural networks for NNE and eliminates the arbitrariness in the manual selection of any single neural network model of a specific configuration, thus maximizing the knowledge utilization without risk of memorization or over-fitting.