基于UPLC-QTOF-MS结合非靶向代谢组学鉴别3种江西名茶

Non-targeted Metabolomics Based on Ultra-high Performance Liquid Chromatography-Quadrupole Time-of-Flight Mass Spectrometry for Discrimination of Three Jiangxi Famous Teas

利用超高效液相色谱-四极杆飞行时间质谱仪建立一种狗牯脑、庐山云雾茶和婺源绿茶品质鉴别的非靶向代谢组学分析方法。利用MassHunter Mass Profile对原始数据进行逐步筛选,最终确定220个特征差异化合物,并对其进行主成分分析和聚类分析,结果表明3种茶叶具有较大差异。构建偏最小二乘判别分析预测模型,该模型能够对狗牯脑、庐山云雾茶和婺源绿茶品质进行鉴别,准确度达100%。通过一级母离子和二级碎片离子对220种特征差异物进行鉴定,最终鉴定出22种,主要成分为黄酮类、糖苷衍生物和有机酸类等,结合热图分析发现其在3种江西名茶中的含量具有明显差异。本研究对茶叶品质鉴别具有一定指导意义,该方法可广泛用于食品的分析和表征。

A non-targeted metabolomics method for identifying the quality of Gougunao tea, Lu Mountain Clouds-Mist tea,and Wuyuan green tea was established by ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry(UPLC-QTOF-MS). The original data were screened by MassHunter Mass Profile, and 220 characteristic differential metabolites were obtained. The results of principal component analysis(PCA) and hierarchical cluster analysis(HCA) showed that there was a clear discrimination among the three teas. Partial least squares discriminant analysis(PLS-DA)was used to establish a prediction model for identifying the quality of tea with an accuracy of 100%. Meanwhile, of the 220 characteristic differential metabolites, 22 were identified, mainly including flavonoids, glycoside derivatives and organic acids. Through heatmap analysis, it was found that their contents were significantly different among tea samples. This study is meaningful for guiding tea quality identification, which can be widely used in food analysis and characterization.