School of Chemical Engineering, Nanjing University of Science and Technology, Nanjing 210094, PR China; China National Quality Supervision and Testing Center for Processed Food (FuZhou), Fujian Inspection and Research Institute for Product Quality, Fuzhou 350002, PR China.
The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, PR China.
Food Chem. 2019 Jun 15;283:73-82. doi: 10.1016/j.foodchem.2019.01.050. Epub 2019 Jan 15.
The taste and aroma quality of Tieguanyin tea fluctuate seasonally and yearly. However, the compounds responsible for the seasonal and year variations of metabolic pattern and its sensory quality are far from clear. 60 Tieguanyin tea samples harvested in different years and seasons were analyzed by ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) and chemometrics. Principal component analysis (PCA) explained 33.2% of the total variance, while orthogonal projection to latent structures discriminate analysis (OPLS-DA) can obtain potential metabolites with better discrimination, and with RX and Q of cross-validation as 0.974 and 0.937, respectively. Subsequently, heat map analysis (HCA) visualized relationships between Tieguanyin teas with these significantly different potential metabolites by Mann-Whitney U test (p < 0.05). Furthermore, the best discriminate metabolites contributing to different sensory qualities were revealed by stepwise liner discrimination analysis (SLDA) with 100% accuracy rate. The present strategy also exhibited great potential for untargeted metabolomics of other foods.
铁观音茶的口感和香气品质随季节和年份而波动。然而，负责代谢模式及其感官品质季节性和年度变化的化合物还远不清楚。采用超高效液相色谱-四极杆飞行时间质谱联用技术（UPLC-Q-TOF/MS）和化学计量学方法对 60 个不同年份和季节收获的铁观音茶样品进行了分析。主成分分析（PCA）解释了总方差的 33.2%，而正交偏最小二乘判别分析（OPLS-DA）可以获得具有更好区分能力的潜在代谢物，交叉验证的 RX 和 Q 分别为 0.974 和 0.937。随后，通过曼-惠特尼 U 检验（p < 0.05）对热图分析（HCA）可视化了这些具有显著差异的潜在代谢物与铁观音茶之间的关系。此外，通过逐步线性判别分析（SLDA）还揭示了对不同感官品质有最佳区分能力的代谢物，准确率达到 100%。该策略还显示出在其他食品的非靶向代谢组学分析方面具有很大的潜力。
