Rapid and Simultaneous Determination of Montelukast, Fexofenadine and Cetirizine Using Partial Least Squares and Artificial Neural Networks Modeling

Abstract

<em>Simultaneous determination of pharmaceutical compounds and accurate quantitative prediction of them are of great interest in the clinical and laboratory-based investigations.This work has focused on a comprehensive comparison of Partial Least-Squares (PLS-1) and Artificial Neural Networks (ANN) as two powerful types of chemometric methods. For this purpose, montelukast (MONT), fexofenadine (FEXO) and cetirizine (CET) were studied as three pharmaceuticals whose UV-Vis absorption spectra highly overlap each other. The cross-validation leave-one-sample-out procedure was applied and the optimum number of factors was determined. The developed models were subsequently validated through testing with an independent dataset. Furthermore, a simple and fast method for wavelength selection (WS-PLS-1) in the calibration step was presented which involved the calculation of the Net Analyte Signal Regression Plot (NASRP)</em><em>for each test sample. Highest prediction accuracies corresponded to WS-PLS-1 method with R² values </em><em>of 0.994, 0.982 and 0.999 for MONT, FEXO and CET, respectively. The best values of detection limit were also provided by WS-PLS-1 method which obtained to be 0.029, 0.049 and 0.054 mg/L for MONT, FEXO and CET, respectively. According to the results obtained, WS-PLS-1 method was shown to have the potential to be utilized as a promising tool in clinical and pharmaceutical applications.</em>