基于气相色谱-质谱联用与液相色谱-质谱联用的非靶向代谢组学用于3类茶叶中化学成分分析（英文）

茶叶中的化学成分是构成其风味特征的物质基础。本研究建立了基于气相色谱-质谱联用和液相色谱-质谱联用的非靶向代谢组学方法。完成预处理及分析条件优化后,使用标准样品考察方法的线性、回收率及重复性,结果表明方法整体稳定且结果可靠。将该方法用于绿茶、乌龙茶和红茶中化学成分的分析。通过超声辅助溶剂提取及气相色谱-质谱联用分析共获得1812个特征峰,而使用加热溶剂提取及液相色谱-质谱联用分析可获得2608个特征峰。结合保留规律及质谱数据库,共定性173种化合物(109种随后经标准样品验证),其中只有9种化合物在上述两类分析中被同时检出,表明方法互补性良好。3类茶叶数据的偏最小二乘判别分析结果表明,三者间存在显著差异。结合模型的变量重要因子(VIP)与非参数检验共筛选出90种化合物,其中包含儿茶素、氨基酸、糖、有机酸和黄酮苷类等众多与茶叶滋味密切相关的化学成分。

Non-targeted metabolomics study for the analysis of chemical compositions in three types of tea by using gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry

Tea is one of the most widely consumed beverages in the world for its benefits to daily life and health. To discover the difference and correlation of chemical compositions in the three typical types of tea, a non-targeted metabolomics method was developed. After the optimization of extraction methods, gas chromatography-time-of-flight mass spectrometry and liquid chromatography-quadrupole time-of-flight mass spectrometry were applied for metabolomics analysis, 1812 and 2608 features were obtained, respectively. By comparing with the known compounds in public and/or commercial databases, 173 compounds were tentatively identified, and 109 of them were experimentally confirmed by standards. Totally, 33 tea samples including 12, 12 and 9 samples of green, oolong and black tea, respectively, were analyzed by using the above two methods. Multivariate analysis, Mann-Whitney U test and hierarchical cluster analysis were used to find and visualize the differential components in the three types of tea. Finally, 90 compounds, which contain catechins, amino acids, organic acids, flavonol glycosides, alkaloids, carbohydrates, lipids, etc, were found with a significant difference among them. This study demonstrates the potentials and power of metabolomics methods to understand the chemical secrets of tea. This should help a lot to optimize the processes of agriculture, storage, preparation and consumption.