Effects of harvest season and altitude on the traceability of Fenghuang Dancong tea: Based on stable isotopes and machine learning
文献类型： 外文期刊
作者： Liu, Wenwen 1 ; Zhao, Jie 1 ; Wei, Wan 3 ; Liang, Shuilian 1 ; Xiao, Lu 1 ; Fu, Manqin 4 ; Wang, Xu 1 ; Chen, Yan 1 ;
作者机构： 1.Guangdong Acad Agr Sci, Inst Qual Stand & Monitoring Technol Agroprod, Guangzhou 510640, Peoples R China
2.Guangdong Prov Key Lab Qual & Safety Risk Assessme, Guangzhou 510640, Peoples R China
3.Guangdong Ocean Univ, Coll Food Sci & Technol, Zhanjiang 524088, Peoples R China
4.Guangdong Acad Agr Sci, Sericultural & Agrifood Res Inst, Guangzhou 510610, Peoples R China
关键词： Harvest season; Altitude; Dancong tea; Stable isotope; Machine learning; Traceability
期刊名称：FOOD CHEMISTRY-X （ 影响因子：8.2； 五年影响因子：8.2 ）
ISSN： 2590-1575
年卷期： 2025 年 28 卷
页码：
收录情况： SCI
摘要： Tea traceability is crucial as its quality and value vary significantly based on harvest season and altitude. In this study, we aimed to identify the harvest season and altitude of Fenghuang Dancong tea using 513C, 515N, 52H, and 518O combined with machine learning. We explored the effects of harvest season, altitude, meteorological factors, and tea variety on stable isotope compositions. The results revealed significant differences in stable isotope ratios among tea samples from different seasons and altitudes, with the influence of harvest season on isotope variations being greater than that of altitude. Additionally, 52H and 518O were identified as key indicators of seasonal differentiation. The support vector machine model showed best performance, achieving classification accuracies exceeding 93 % in discriminating high-value Dancong tea from spring and high-altitude regions. This study demonstrates that integrating stable isotopes with machine learning offers promising approaches for monitoring the authenticity of tea.
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