Modeling the electrophoretic mobility of beta-blockers in capillary electrophoresis using artificial neural networks

Artificial neural networks were used for modeling the mobility of five beta-blockers (i.e., labetalol atenolol, practolol, timolol and propranolol) in running buffer with ternary solvent background electrolyte systems containing 80 mM acetate buffer dissolved in water, methanol, ethanol and their ternary mixtures. The volume fractions of two solvents (f(2), f(3)) and cologarithm of electrophoretic mobilities in pure solvents (i.e., -Lnmu(1), -Lnmu(2) and -Lnmu(3)) were used as inputs and cologarithm of the mobility in mixed solvents was the output of the networks. The number of neurons in hidden layer, learning rate, momentum and the number of epochs were optimized, in which two neurons in hidden layer, 0.2, 0.9 and 20000 were found the optimized values for learning rate, momentum and number of epochs, respectively. Mean percentage deviations (MPD) between calculated and experimental mobilities were computed as an accuracy criterion. To assess the correlative ability of the model, all data points in each set were used as training set and the mobilities were back-calculated by the trained networks, in which the overall MPD (OMPD)+/- standard deviation (SD) for correlative study was 3.1+/- 2.3. To evaluate the prediction capability of the proposed ANN model, the network was trained using 15 data points for each analyte and the remaining data points were predicted. The obtained OMPD (+/-SD) for this analysis was 3.6+/-3.0. To further investigate on the applicability of ANN, a generalized network was trained with 10 data points from each beta-blocker and then the network was employed to predict the mobilities of the analytes in ternary solvent electrolyte systems. The MPDs for predicted mobilities were 3.6%, 3.6%, 3.9%, 3.7% and 2.9% respectively for labetalol, atenolol, practolol, timolol and propranolol.