首页|Artificial neural network and multivariate calibration methods assisted UV spectrophotometric technique for the simultaneous determination of metformin and Pioglitazone in anti-diabetic tablet dosage form
Artificial neural network and multivariate calibration methods assisted UV spectrophotometric technique for the simultaneous determination of metformin and Pioglitazone in anti-diabetic tablet dosage form
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NSTL
Elsevier
? 2021In this paper, for the simultaneous measurement of Metformin (MET) and Pioglitazone (PIO) in Actoplus MET as antidiabetic tablet, chemometrics and spectrophotometry methods without the need for separation steps was used. The applied chemometrics methods were artificial neural network (ANN), partial least squares regression (PLS), and principal component regression (PCR). The ANN consisting of two, four and six layers with 2, 4, 6, 8, and 10 neurons was trained using a feed forward back-propagation (FFBP) learning. The algorithms used were Levenberg-Marquardt (LM) and gradient descent with momentum and adaptive learning rate back propagation (GDX). The mean square error (MSE) of the LM algorithm was obtained 3.18 ?× ?10?30 and 1.58 ?× ?10?30 for MET and PIO, respectively, which represented that the LM algorithm performed better than the GDX algorithm. In the PLS method, lower root mean square error (RMSE) (MET ?= ?0.0558, PIO ?= ?0.3981) showed better performance compared PCR method (MET ?= ?0.0559, PIO ?= ?0.4048). Finally, the results of the proposed methods and high performance liquid chromatography (HPLC) as a reference approach were compared with one-way ANOVA test at 95% confidence level, which did not show a significant difference between the data.
Artificial neural networkMetforminPartial least squares regressionPioglitazonePrincipal component regressionSpectrophotometry
Arabzadeh V.、Sohrabi M.R.
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Department of Chemistry Jouybar Branch Islamic Azad University
Department of Chemistry North Tehran Branch Islamic Azad University
NSTL
Elsevier
