Speaker
Description
Marbofloxacin is a potent antibiotic synthesized for veterinary use and treatment of advanced infections, which is often dosed in combination with other substances such as clotrimazole and dexamethasone. UV/Vis spectrophotometry, with its widespread and available instrumentation in research and testing laboratories, along with simple sample preparation, offers rapid analysis, which enables its application in quality control of pharmaceutical and veterinary preparations.
But simultaneous determination of these three compounds without prior separation can be challenging due to extensive overlapping spectra. In order to minimize the influence of the overlapping spectra on the results, principal components (PCs) extracted from the original UV spectra were used for the training of counter-propagation artificial neural networks (CPANN). The performances of the CPANN models were examined by (1) changing the number of principal components used for training as well as (2) the relative importance of the selected PCs. Cross-validation was used for control of the generalization performances of the CPANN models.
The developed models have excellent predictive performance for all three compounds with simultaneous quantification of marbofloxacin and clotrimazole with correlation coefficients of 0.99 for calibration and dexamethasone with a correlation coefficient of 0.95.
The results presented in this work demonstrate that analyzing complex mixtures is achievable using inexpensive instrumentation. The proposed UV spectrophotometric method assisted by CPANN represents a cost-effective tool for multicomponent pharmaceutical analysis, which has potential application in routine analysis in quality control laboratories while implementing artificial intelligence in analytical chemistry.
Keywords: Marbofloxacin, clotrimazole, dexamethasone, counter-propagation artificial neural networks, chemometrics, multicomponent analysis.
References:
1. M. Liu, The application of ultraviolet-visible spectrophotometry in pharmaceutical analysis, Appl. Comput. Eng. 159 (2025) 85–92. https://doi.org/10.54254/2755-2721/2025.23761
2. V. Cerdà, P. Phansi, S. Ferreira, From mono-to multicomponent methods in UV-VIS spectrophotometric and fluorimetric quantitative analysis – A review, TrAC Trends Anal. Chem. 157 (2022) 116772. https://doi.org/10.1016/j.trac.2022.116772