Abstract
Tea, a nutritionally and culturally vital beverage, demands reliable quality grading and cultivar authentication, particularly for high-value functional varieties. Yet the complex tea matrix and subtle compositional differences among closely related cultivars render on-site identification challenging. While nanopore sensing shows great potential, its application to tea analysis remains unexplored. Herein, we present a rapid single-molecule sensing strategy using phenylboronic acid-modified MspA-90PBA nanopores, achieving accurate tea grading and cultivar discrimination via anthocyanin–catechin dual-component fingerprinting. A total of 10 tea polyphenols (2 anthocyanins and 8 catechins) generate distinct current signals. A machine-learning model reaches 94.2% accuracy for polyphenol identification in complex tea matrixes. This platform precisely discriminates anthocyanin-enriched Zijuan tea from its closely related Ziya tea cultivar and grades Zijuan samples of various quality levels. This high-precision approach provides an effective tool for tea authentication, quality evaluation, and adulteration detection, with broad potential for rapid natural product assessment.
| Original language | English |
|---|---|
| Pages (from-to) | 8542-8550 |
| Number of pages | 9 |
| Journal | Nano Letters |
| Volume | 26 |
| Issue number | 26 |
| DOIs | |
| State | Published - 8 Jul 2026 |
| Externally published | Yes |
Keywords
- fingerprinting
- machine learning
- nanopore sensing
- tea authentication
- tea polyphenols
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