Application of silver nanoparticles and principal component-artificial neural network models for simultaneous determination of levodopa and benserazide hydrochloride by a kinetic spectrophotometric method.

A multicomponent analysis method based on principal component analysis-artificial neural network model (PC-ANN) is proposed for the simultaneous determination of levodopa (LD) and benserazide hydrochloride (BH). The method is based on the reaction of levodopa and benserazide hydrochloride with silver nitrate as an oxidizing agent in the presence of PVP and formation of silver nanoparticles. The reaction monitored at analytical wavelength 440 nm related to surface plasmon resonance band of silver nanoparticles. Differences in the kinetic behavior of the levodopa and benserazide hydrochloride were exploited by using principal component analysis, an artificial neural network (PC-ANN) to resolve concentration of analytes in their mixture. After reducing the number of kinetic data using principal component analysis, an artificial neural network consisting of three layers of nodes was trained by applying a back-propagation learning rule. The optimized ANN allows the simultaneous determination of analytes in mixtures with relative standard errors of prediction in the region of 4.5 and 6.3 for levodopa and benserazide hydrochloride respectively. The results show that this method is an efficient method for prediction of these analytes. Copyright © 2011 Elsevier B.V. All rights reserved.

Citation

J Tashkhourian, M R Hormozi-Nezhad, J Khodaveisi. Application of silver nanoparticles and principal component-artificial neural network models for simultaneous determination of levodopa and benserazide hydrochloride by a kinetic spectrophotometric method. Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy. 2011 Nov;82(1):25-30

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PMID: 21820350

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