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Research on electrochemical fingerprint detection of tea beverage based on particle swarm optimization algorithm

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Abstract

Electrochemical fingerprinting coupled with chemometrics holds promise for food analysis and authentication. This study evaluated the potential of particle swarm optimization (PSO) for feature selection from voltammetric fingerprints for identification of green, black and oolong teas. Cyclic voltammetry, square wave voltammetry and differential pulse voltammetry were utilized to obtain characteristic electrochemical profiles reflecting the phytochemical compositions. The voltammograms displayed well-defined oxidation peaks corresponding to major tea polyphenols including catechins in green tea and theaflavins in black tea. Peak currents followed the order Ip,A3 > Ip,A4 > Ip,A1 indicating higher theaflavin content in black tea compared to catechins in green and oolong varieties. Excellent reproducibility was achieved with relative standard deviation < 5% for triplicate measurements. This demonstrated the reliability of voltammetric fingerprints for qualitative and quantitative analysis of electroactive tea components. PSO was implemented for feature selection from the high-dimensional voltammetric data. Optimized parameters included inertia weight variation, swarm size of 50, acceleration factors of 2, constriction coefficient of 0.729 and 100 iterations. PSO converged after 40 iterations, reaching minimum cross-validation error at 92 iterations. 15 optimal features were selected including peak potentials, currents, areas, widths and heights representing key oxidation signals. PSO effectively identified the most informative features for discriminating between the tea types. Overall, the study highlighted the benefits of integrating PSO feature selection with voltammetric profiling for robust sample identification. Further chemometric modeling can be investigated for authenticity evaluation and quality control of tea and other agricultural products using this rapid, cost-effective electrochemical approach.

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  1. Network Information Center, Zibo Vocational Institute, Zibo, 255314, Shandong, China

    Xuexiang Li

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Li, X. Research on electrochemical fingerprint detection of tea beverage based on particle swarm optimization algorithm. Food Measure 18, 1355–1362 (2024). https://doi.org/10.1007/s11694-023-02294-9

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