Nature inspired computation and ensemble neural network to build a robust model for spectral data

UV spectrophotometry was introduced for simultaneous determination of Itraconazole (ITZ) and Secnidazole (SEZ) in their mixture without any prior separation. In this study, fourteen nature-inspired algorithms combined with partial least squares (PLS) regression were used as baseline algorithms. Then, an ensemble neural networks model was introduced. The performance of the models was evaluated using parameters like the root average squared error (RASE), coefficient of determination (R²), and The average absolute error (AAE). RASE, R² and AAE values of (0.1131, 0.9995, and 0.0819) and (0.1798, 0.9954, and 0.1365) were obtained for calibration and test sets of ITZ, respectively. RASE, R² and AAE values of (0.5812, 0.9962, and 0.4360) and (0.4903, 0.9957 and 0.3917) were obtained for calibration and test sets of SEZ, respectively. The models in this study can be useful for the researchers who are interested to work on the simultaneous determination of active ingredients in pharmaceutical dosage forms using UV spectroscopy. The proposed method was applied to the pharmaceutical dosage form.