Literature DB >> 26513228

Advanced stability indicating chemometric methods for quantitation of amlodipine and atorvastatin in their quinary mixture with acidic degradation products.

Hany W Darwish1, Said A Hassan2, Maissa Y Salem3, Badr A El-Zeany3.   

Abstract

Two advanced, accurate and precise chemometric methods are developed for the simultaneous determination of amlodipine besylate (AML) and atorvastatin calcium (ATV) in the presence of their acidic degradation products in tablet dosage forms. The first method was Partial Least Squares (PLS-1) and the second was Artificial Neural Networks (ANN). PLS was compared to ANN models with and without variable selection procedure (genetic algorithm (GA)). For proper analysis, a 5-factor 5-level experimental design was established resulting in 25 mixtures containing different ratios of the interfering species. Fifteen mixtures were used as calibration set and the other ten mixtures were used as validation set to validate the prediction ability of the suggested models. The proposed methods were successfully applied to the analysis of pharmaceutical tablets containing AML and ATV. The methods indicated the ability of the mentioned models to solve the highly overlapped spectra of the quinary mixture, yet using inexpensive and easy to handle instruments like the UV-VIS spectrophotometer.
Copyright © 2015 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Amlodipine; Artificial neural networks; Atorvastatin; Genetic algorithm; Partial Least Squares; Stability indicating

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Year:  2015        PMID: 26513228     DOI: 10.1016/j.saa.2015.10.007

Source DB:  PubMed          Journal:  Spectrochim Acta A Mol Biomol Spectrosc        ISSN: 1386-1425            Impact factor:   4.098


  1 in total

1.  Characterization of two new degradation products of atorvastatin calcium formed upon treatment with strong acids.

Authors:  Jürgen Krauß; Monika Klimt; Markus Luber; Peter Mayer; Franz Bracher
Journal:  Beilstein J Org Chem       Date:  2019-09-02       Impact factor: 2.883

  1 in total

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