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<p>Authenticating specialty tea products remains a critical challenge in premium food markets, yet current analytical approaches are constrained by limited reproducibility and susceptibility to instrumental variation. Here, we present a deep learning framework that transforms liquid chromatography–mass spectrometry (LC–MS) metabolomic data into image representations, enabling robust authentication of tea products under real-world analytical conditions. Profiling 274 Tieguanyin tea samples across seasonal harvests (spring and autumn) and processing methods (light-scented and strong-scented), our approach achieved 90.9% (95% confidence interval [CI]: 80.4%–96.0%) classification accuracy—substantially outperforming conventional multivariate and machine learning methods (sPLS-DA: 85.5%; random forest: 87.3%). Critically, when subjected to chromatographic drift—a pervasive source of analytical irreproducibility—our model maintained 78.2% accuracy while traditional methods degraded to 69.1%. This framework addresses fundamental limitations in untargeted metabolomics, offering a generalizable solution for food authentication that extends beyond tea to broader applications in agricultural product verification and systems biology.</p>

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Deep learning enable precision authentication of seasonal and processing signatures in tieguanyin tea

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月10日

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  • Chao Zheng,
  • Xiaohe Zhou,
  • Ningning Shao,
  • Jiayi Cheng,
  • Wei Xin,
  • Ying Liu,
  • Junling Zhou

摘要

Authenticating specialty tea products remains a critical challenge in premium food markets, yet current analytical approaches are constrained by limited reproducibility and susceptibility to instrumental variation. Here, we present a deep learning framework that transforms liquid chromatography–mass spectrometry (LC–MS) metabolomic data into image representations, enabling robust authentication of tea products under real-world analytical conditions. Profiling 274 Tieguanyin tea samples across seasonal harvests (spring and autumn) and processing methods (light-scented and strong-scented), our approach achieved 90.9% (95% confidence interval [CI]: 80.4%–96.0%) classification accuracy—substantially outperforming conventional multivariate and machine learning methods (sPLS-DA: 85.5%; random forest: 87.3%). Critically, when subjected to chromatographic drift—a pervasive source of analytical irreproducibility—our model maintained 78.2% accuracy while traditional methods degraded to 69.1%. This framework addresses fundamental limitations in untargeted metabolomics, offering a generalizable solution for food authentication that extends beyond tea to broader applications in agricultural product verification and systems biology.