Principal component articial neural network calibration models for the simultaneous spectrophotometric estimation of mefenamic acid and paracetamol in tablets
| Autor: | Satyanarayana Dondeti, Kannan Kamarajan, Manavalan Rajappan |
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| Jazyk: | angličtina |
| Rok vydání: | 2006 |
| Předmět: | |
| Zdroj: | Journal of the Serbian Chemical Society, Vol 71, Iss 11, Pp 1207-1218 (2006) |
| Druh dokumentu: | article |
| ISSN: | 0352-5139 1820-7421 |
| DOI: | 10.2298/JSC0611207S |
| Popis: | Simultaneous estimation of all drug components in a multicomponent analgesic dosage form with artificial neural networks calibration models using UV spectrophotometry is reported as a simple alternative to using separate models for each component. A novel approach for calibration using a compound spectral dataset derived from three spectra of each component is described. The spectra of mefenamic acid and paracetamol were recorded as several concentrations within their linear range and used to compute a calibration mixture between the wavelengths 220 to 340 nm. Neural networks trained by a Levenberg-Marquardt algorithm were used for building and optimizing the calibration models using MATALAB® Neural Network Toolbox and were compared with the principal component regression model. The calibration models were thoroughly evaluated at several concentration levels using 104 spectra obtained for 52 synthetic binary mixtures prepared using orthogonal designs. The optimized model showed sufficient robustness even when the calibration sets were constructed from a different set of pure spectra of the components. The simultaneous prediction of both components by a single neural network with the suggested calibration approach was successful. The model could accurately estimate the drugs, with satisfactory precision and accuracy, in tablet dosage with no interference from excipients as indicated by the results of a recovery study. |
| Databáze: | Directory of Open Access Journals |
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