Simultaneous spectrophotometric determination of diclofenac potassium and methocarbamol in binary mixture using chemometric techniques and artificial neural networks.
Drug Test Anal. 2011 Apr; 3(4):228-33.DT

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.

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Publisher Full Text (DOI)

Authors+Show Affiliations

Elkady EF

Pharmaceutical Chemistry Department, Faculty of Pharmacy, Cairo University, Kasr El-Aini St, Cairo 11562, Egypt. ehabelkady75@gmail.com

MeSH

Chemistry, PharmaceuticalDiclofenacMethocarbamolNeural Networks, ComputerSpectrophotometry, Ultraviolet

Pub Type(s)

Comparative Study

Journal Article

Language

eng

PubMed ID

21500367

Citation

Elkady, Ehab F.. "Simultaneous Spectrophotometric Determination of Diclofenac Potassium and Methocarbamol in Binary Mixture Using Chemometric Techniques and Artificial Neural Networks." Drug Testing and Analysis, vol. 3, no. 4, 2011, pp. 228-33.

Elkady EF. Simultaneous spectrophotometric determination of diclofenac potassium and methocarbamol in binary mixture using chemometric techniques and artificial neural networks. Drug Test Anal. 2011;3(4):228-33.

Elkady, E. F. (2011). Simultaneous spectrophotometric determination of diclofenac potassium and methocarbamol in binary mixture using chemometric techniques and artificial neural networks. Drug Testing and Analysis, 3(4), 228-33. https://doi.org/10.1002/dta.216

Elkady EF. Simultaneous Spectrophotometric Determination of Diclofenac Potassium and Methocarbamol in Binary Mixture Using Chemometric Techniques and Artificial Neural Networks. Drug Test Anal. 2011;3(4):228-33. PubMed PMID: 21500367.

* Article titles in AMA citation format should be in sentence-case

TY - JOUR T1 - Simultaneous spectrophotometric determination of diclofenac potassium and methocarbamol in binary mixture using chemometric techniques and artificial neural networks. A1 - Elkady,Ehab F, Y1 - 2010/12/29/ PY - 2010/08/26/received PY - 2010/09/28/revised PY - 2010/09/28/accepted PY - 2011/4/19/entrez PY - 2011/4/19/pubmed PY - 2012/3/7/medline SP - 228 EP - 33 JF - Drug testing and analysis JO - Drug Test Anal VL - 3 IS - 4 N2 - 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. SN - 1942-7611 UR - https://www.unboundmedicine.com/medline/citation/21500367/Simultaneous_spectrophotometric_determination_of_diclofenac_potassium_and_methocarbamol_in_binary_mixture_using_chemometric_techniques_and_artificial_neural_networks_ L2 - https://doi.org/10.1002/dta.216 DB - PRIME DP - Unbound Medicine ER -

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Simultaneous spectrophotometric determination of diclofenac potassium and methocarbamol in binary mixture using chemometric techniques and artificial neural networks.Drug Test Anal. 2011 Apr; 3(4):228-33.DT