ArticleBMC chemistry2026
Artificial intelligence-assisted UV-Vis spectrophotometric method for simultaneous quantification of naringin and naringenin in Thymus canoviridis Jalas.
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
backgroundThis study aimed to develop an artificial intelligence (AI)-based UV-Vis spectrophotometric method for simultaneously quantifying naringin and naringenin in Thymus canoviridis methanol extract. Conventional spectrophotometric techniques often face challenges in analyzing mixtures of compounds with overlapping spectra, making accurate quantification difficult. This limitation calls for advanced modelling approaches that incorporate machine learning to enhance prediction accuracy while aligning with green chemistry principles.
methodsBinary mixtures of naringin and naringenin were prepared at concentrations ranging from 5 to 40 μg/mL. UV-Vis spectra were collected over the 240-450 nm range. The trained machine learning models included Support Vector Regression (SVR), Linear Regression, Lasso Regression, Ridge Regression, and Elastic Net. These models were used to predict the concentrations of both flavonoids from their spectral data. Performance was evaluated using Mean Absolute Error (MAE) and the coefficient of determination (R
resultsThe Ridge Regression model showed superior predictive performance, achieving MAEs of 0.882 for naringin and 0.378 for naringenin, with R
conclusionThis study presents a green UV-Vis spectrophotometric method enhanced by AI for the simultaneous quantification of naringin and naringenin in plant extracts. By leveraging AI for spectral differentiation, the method achieves high accuracy and reliability without requiring chemical separation. This approach represents a significant advancement in plant metabolite analysis, facilitating high-throughput screening in phytochemical studies. The method's minimal solvent use, low waste generation, and energy efficiency make it a sustainable alternative to traditional techniques, suited for environmentally conscious phytochemical analysis.
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