L and D enantiomer binary mixture determination simultaneously by spectrophotometric method without separation step based on artificial neural network and least squares support vector machine in valsartan pharmaceutical production

Abstract

In this study, spectrophotometry method as a quick, simple, and accurate approach was proposed for the simultaneous determination of Valsartan (VAL) and its enantiomeric impurity (IMP-A) in binary mixtures using feed-forward artificial neural network (FFANN) with backpropagation learning algorithm and least squares support vector machine (LS-SVM) techniques without using excess solvent and the time-consuming extraction phase. Two different algorithms, including Levenberg–Marquardt (LM) and scaled conjugate gradient (SCG) as training algorithms was used in ANN method. LM algorithm with 2 layers, 9 neurons and 2 layers, 7 neurons in the FFNN with the mean square error (MSE) of 2.84 × 10−11 and 5.08 × 10−12 had the best response for predicting VAL and IMP-A concentrations, respectively. By optimizing regularization parameter (γ) and width of the function (σ) in LS-SVM model, mean recovery percentage and root mean square error (RMSE) were obtained 98.61, 98.95 and 3.22 × 10−4, 2.89 × 10−4 for VAL and IMP-A, respectively. The achieved results from the commercial tablet (Adovan) analysis using the proposed methods were compared to the yielded by HPLC technique via one-way analysis of variance (ANOVA) test. According to the results, they revealed good agreement and there was no significant differences.