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<p><i>Lithocarpus litseifolius</i> (sweet tea) is a medicinal and edible plant rich in flavonoids and essential nutrients, with potential as a hepatoprotective beverages and natural sweetener. Although widely cultivated across several provinces in China, the quality and consistency of its raw material remain poorly regulated. To address this, 163 samples (<i>n</i> ≥ 18) from 7 main producing regions were analyzed for 22 functional compounds, 4 stable isotope ratios, and 49 multi-element to discriminate cultivation practices and geographical origins. Orthogonal partial least squares discriminant analysis (OPLS-DA) successfully generated prediction models across two cultivation regions. Integrating 8 machine-learning algorithms with multi-level data fusion identified 6 key variables—caffeine, Rb, Ce, δ¹⁵N, Sr, and 3”-O-acetylphlorizin. Five base learners built on these variables were then combined via soft-voting ensemble learning, yielding an optimal origin classifier with 100.00% accuracy. Additionally, the study delivered the first comprehensive analysis of quality variations in sweet tea and identified seven primary influenced environmental factors, offering insights into cultivation strategies and quality formation mechanisms.</p>

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Tracing origin and cultivation practice of Lithocarpus litseifolius via multi-data fusion and machine learning approaches

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月13日

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  • Yifan Tang,
  • Ping Yu,
  • Feng Xiong,
  • Zhilai Zhan,
  • Kai Xie,
  • Shuyan Yu,
  • Yifan Ning,
  • Zhanhan Zhou,
  • Chun Wang,
  • Weisen Qian,
  • Xiwen Zhang,
  • Yike Liang,
  • Ruijiao Wang,
  • Guoxia Han,
  • Jian Yang

摘要

Lithocarpus litseifolius (sweet tea) is a medicinal and edible plant rich in flavonoids and essential nutrients, with potential as a hepatoprotective beverages and natural sweetener. Although widely cultivated across several provinces in China, the quality and consistency of its raw material remain poorly regulated. To address this, 163 samples (n ≥ 18) from 7 main producing regions were analyzed for 22 functional compounds, 4 stable isotope ratios, and 49 multi-element to discriminate cultivation practices and geographical origins. Orthogonal partial least squares discriminant analysis (OPLS-DA) successfully generated prediction models across two cultivation regions. Integrating 8 machine-learning algorithms with multi-level data fusion identified 6 key variables—caffeine, Rb, Ce, δ¹⁵N, Sr, and 3”-O-acetylphlorizin. Five base learners built on these variables were then combined via soft-voting ensemble learning, yielding an optimal origin classifier with 100.00% accuracy. Additionally, the study delivered the first comprehensive analysis of quality variations in sweet tea and identified seven primary influenced environmental factors, offering insights into cultivation strategies and quality formation mechanisms.