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Coffee geographical origin rapid on-site discrimination strategy using an electrochemical fingerprinting platform by
Chengxiang Yong1, Guohua Hui1, Zhaoyi Sun1
1College of Artificial Intelligence, Key Laboratory of Forestry Sensing Technology and Intelligent Equipment of Department of Forestry, Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Zhejiang A & F University, Hangzhou, Zhejiang 311300, China.
Abstract:
Coffee geographical origin discrimination is essential for quality control and market regulation. In this study, an electrochemical fingerprinting strategy was established by integrating electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), and chronoamperometry (i-t). A copper-film electrode was selected based on EIS, scan-rate-dependent CV, and apparent double-layer capacitance analyses, while 0.01 mol/L NaOH and 35 °C were identified as the optimal operating conditions, effectively minimizing carbohydrate-derived electrochemical interference and enabling robust extraction of origin-specific electrochemical fingerprints. Current-magnitude responses from 13 Arabica coffee origins were transformed into 20-dimensional fingerprint features using eight independently prepared extracts per origin. Statistical analysis confirmed significant origin-dependent differences. Further validation using permutational multivariate analysis of variance (PERMANOVA), PCA, LDA, and an SVM classifier enabled reliable discrimination, with the SVM achieving an overall accuracy of 92.31%. The platform also exhibited good inter-week reproducibility and operational stability, offering a rapid, low-cost approach for coffee geographical origin authentication.
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