基于化学成分全扫描与多元统计的三峡库区绿茶特征差异化合物分析
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国家自然科学基金青年基金项目(No.32502353);重庆市教育委员会科学技术研究重大项目(No.KJZD-M202415101);四川省中央引导地方科技发展专项(No.2024ZYD0047);重庆市科学技术局科研院所绩效激励引导专项(No.zzkjjh2024016);重庆市卫生健康委员会科卫联合中医药重点项目(No.2025ZYZD006);大学生创新训练计划项目(No.X202410637019,No.X202410637020);重庆市技术创新与应用发展专项(No.CSTB2025TIAD-qykjggX0430)


Analysis of Characteristic Compounds of Green Tea from Three Gorges Reservoir Area Based on Multivariate Statistical Analysis and Full Scan of Chemical Composition
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    摘要:

    为实现对茶叶原产地产品的区分和保护,建立了一种基于质谱全扫描结合系统聚类的渝产茶叶成分差异分析方法。以三峡库区绿茶为研究对象,采用高效液相色谱串联高分辨质谱进行化学成分全扫描分析,筛选确认茶叶化学成分65种;结合主成分分析和偏最小二乘法建立茶叶地理来源与成分关联的相关性分析模型,筛选得到与地理信息相关的特征成分13个;通过进一步的定量分析,最终确定表儿茶素、表没食子儿茶素没食子酸酯、茶碱、谷氨酸、槲皮素、咖啡酸、芦丁、绿原酸和没食子酸在不同地理来源的茶叶间具有良好的区分。该方法高效、稳定,专属性强,为茶叶产品物质基础与地理溯源研究提供补充。

    Abstract:

    In order to distinguish and protect the tea products of origin, this study established a method of tea composition difference analysis based on mass spectrometry full scanning combined with systematic clustering. Green tea from Three Gorges Reservoir Area was chosen to be the research object. High-performance liquid chromatography tandem high-resolution mass spectrometry was used for chemical full-scan analysis, and 65 chemical components of tea were confirmed; combining principal component analysis and partial least squares method, a correlation analysis model of geographical origin and component correlation of tea was established. 13 feature components related to geographical information were screened. Through further quantitative analysis, it is finally determined that epicatechin, EGCG, theophylline, glutamate, quercetin, caffeic acid, rutin, chlorogenic acid and gallic acid have a good distinction among the selected tea from different geographical sources. This method is efficient, stable and specific, and provides a supplement for the study of the material basis and geographical traceability of tea products.

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秦采奕,郭延垒,龙成燕,张莉,白亚敏,张磊.基于化学成分全扫描与多元统计的三峡库区绿茶特征差异化合物分析[J].重庆师范大学学报自然科学版,2026,43(1):81-92

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  • 在线发布日期: 2026-04-16