Partial Least-Squares and Linear Support Vector Regression Chemometric Methods for Simultaneous Determination of Amoxicillin Trihydrate and Dicloxacillin Sodium in the Presence of Their Common Impurity

Author:

Naguib Ibrahim A1,Abdelaleem Eglal A2,Zaazaa Hala E3,Hussein Essraa A1

Affiliation:

1. Beni-Suef University, Faculty of Pharmacy, Pharmaceutical Analytical Chemistry Department, Alshaheed Shehata Ahmad Hegazy St, Beni-Suef 62514, Egypt; University of Tabuk, Faculty of Pharmacy, Pharmaceutical Chemistry Department, Tabuk 71491, Kingdom of Saudi Arabia

2. Beni-Suef University, Faculty of Pharmacy, Pharmaceutical Analytical Chemistry Department, Alshaheed Shehata Ahmad Hegazy St, Beni-Suef 62514, Egypt

3. Cairo University, Faculty of Pharmacy, Analytical Chemistry Department, Kasr El-Aini, Cairo 11562, Egypt

Abstract

Abstract Two multivariate chemometric models, namely, partial least-squares regression (PLSR) and linear support vector regression (SVR), are presented for the analysis of amoxicillin trihydrate and dicloxacillin sodium in the presence of their common impurity (6-aminopenicillanic acid) in raw materials and in pharmaceutical dosage form via handling UV spectral data and making a modest comparison between the two models, highlighting the advantages and limitations of each. For optimum analysis, a three-factor, four-level experimental design was established, resulting in a training set of 16 mixtures containing different ratios of interfering species. To validate the prediction ability of the suggested models, an independent test set consisting of eight mixtures was used. The presented results show the ability of the two proposed models to determine the two drugs simultaneously in the presence of small levels of the common impurity with high accuracy and selectivity. The analysis results of the dosage form were statistically compared to a reported HPLC method, with no significant difference regarding accuracy and precision, indicating the ability of the suggested multivariate calibration models to be reliable and suitable for routine analysis of the drug product. Compared to the PLSR model, the SVR model gives more accurate results with a lower prediction error, as well as high generalization ability; however, the PLSR model is easy to handle and fast to optimize.

Publisher

Oxford University Press (OUP)

Subject

Pharmacology,Agronomy and Crop Science,Environmental Chemistry,Food Science,Analytical Chemistry

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