Intelligent evaluation of antioxidant activity in tea products based on machine learning and cyclic voltammetry

Cyclic voltammetry coupled with machine learning (ML) was leveraged as a rapid analytical approach to evaluate antioxidant capacities in tea products. Characteristic voltammetric fingerprints were extracted from four tea types and transformed into ML-compatible datasets. The anodic peak currents showed excellent linear scan rate dependence indicating adsorption-controlled interfacial kinetics. Among supervised models, support vector machines demonstrated optimal accuracy of 93.5% and correlation coefficient of 0.96 to spectrophotometric assay values on validation samples. The approach reliably predicted antioxidant contents in commercial teas within 5% relative errors. Case studies on adulteration detection, shelf-life analysis and novel variant screening further proved practical utility. The salient input features were identified to be the oxidation peak regions, validating underlying electron-transfer phenomena. This work successfully demonstrated proof-of-concept for an inexpensive voltammetry-ML platform as an automated, high-throughput analytical tool for functional food testing. With appropriate re-training, it can enable rapid quality assurance to support rising nutraceutical consumer awareness. Prospects include extensive validation on diverse food commodities and field deployment. Overall, this rapid analytical strategy can facilitate point-of-use assessment of antioxidant capacity in functional foods and beverages.