In this study, synchronous fluorescence spectroscopic methods coupled with chemometric techniques were developed and evaluated for the simultaneous quantification of ezetimibe and propranolol, two commonly prescribed cardiovascular drugs. Both drugs exhibit overlapping native fluorescence, posing a challenge for their selective determination. To address this, chemometric models including partial least squares (PLS) and genetic algorithm-based variable selection (GA) were constructed using a calibration dataset based on a 5 factorial design resulting in 25 synthetic mixtures. The developed method has been optimized to account for factors such as solvent composition, micellar systems, and excitation/emission wavelengths that affect the fluorescence signals. The PLS and GA-PLS models were validated using an independent test set of 13 samples based on central composite design revealing the GA-PLS model provided improved quantitative performance with relative root mean square error of prediction (RRMSEP) values of 1.3939 and 1.0005 % for ezetimibe and propranolol, respectively, compared to 2.2502 and 2.3526 % for the PLS models. Hence, the GA-PLS models were successfully applied for the determination of ezetimibe and propranolol in pharmaceutical formulations and spiked plasma samples. Furthermore, the greenness and blueness of the proposed methods were compared against reported HPLC procedures using the AGREE and BAGI tools, revealing a greener analytical footprint for the developed method and higher analytical practicability posing as an environmental-friendly alternative to the standard HPLC technique.

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http://dx.doi.org/10.1016/j.saa.2024.125447DOI Listing

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