ArticleBMC chemistry2026
Chemometric and learning-based multivariate models for quantifying a challenging quaternary mixture of bupropion, dextromethorphan, and their related impurities by UV-Vis spectrophotometry.
Article in BMC chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This study presents a robust, green, sustainable and time-efficient approach for the simultaneous determination of Bupropion HCl (BUP) and Dextromethorphan HBr (DEX) along with their related impurities 3-Chlorobenzoic acid and N, N-Dimethylaniline. Principal component regression (PCR) and partial least-squares (PLS), in addition to advanced chemometric models, namely multivariate curve resolution-alternating least squares (MCR-ALS), and artificial neural networks (ANN), are the four green smart multivariate spectrophotometric models that were proposed and validated. The suggested models were successful in examining the mixture of BUP and DEX in the presence of their impurities. Therefore, the suggested analytical methods can be applied to pharmaceutical formulation analysis without the need for a separation step. The proposed strategy offers a novel analytical platform for quality control laboratories to manage complex formulations involving interfering substances. To further ensure greenness and sustainability of the proposed approach, several assessment tools were applied, including the Modified National Environmental Methods Index (NEMI), Eco-Scale, the Analytical GREEnness (AGREE) metric, the Hexagon algorithm, the Green Analytical Procedure Index (GAPI), the Modified GAPI (MoGAPI), the Blue Applicability Grade Index (BAGI), White Analytical Chemistry (WAC), and the Click Analytical Chemistry Index (CACI). Many traditional analytical techniques pose undesirable dangers to the environment and the analyst, such as using hazardous solvents. This gave analysts the incentive to use green methodologies that take into account the use of safe chemicals, the production of the least amount of trash, substantial time savings, and enhanced analyst safety.
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