Abstract

In this study, the simultaneous determination of diclofenac potassium (DP) and methocarbamol (MT) by chemometric approaches and artificial neural networks using UV spectrophotometry has been reported as a simple alternative to using separate models for each component. Three chemometric techniques—classical least-squares (CLS), principal component regression (PCR), and partial least-squares (PLS)—along with radial basis function-artificial neural network (RBF-ANN) were prepared by using the synthetic mixtures containing the two drugs in methanol. A set of synthetic mixtures of DP and MT was evaluated and the results obtained by the application of these methods were discussed and compared. In CLS, PCR, and PLS, the absorbance data matrix corresponding to the concentration data matrix was obtained by the measurements of absorbances in the range 260–310 nm in the intervals with Δλ = 0.2 nm in their zero-order spectra. Then, calibration or regression was obtained by using the absorbance data matrix and concentration data matrix for the prediction of the unknown concentrations of DP and MT in their mixtures. In RBF-ANN, the input layer consisting of 251 neurons, 9 neurons in the hidden layer, and 2 output neurons were found appropriate for the simultaneous determination of DP and MT. The accuracy and the precision of the four methods have been determined and they have been validated by analyzing synthetic mixtures containing the two drugs. The proposed methods were successfully applied to a pharmaceutical formulation containing the examined drugs. Verf.-Referat