PDF(Pubmed)
Abstract：
Mechanization is the inevitable future of tea harvesting, but its impact on tea chemistry and quality remains uncertain. Our study examines untargeted metabolomic data from 185 oolong tea products (Tieguanyin) made from leaves harvested by hand or machine based on UPLC-QToF-MS analysis. The data revealed a minimum 50% loss for over half of the chemicals in the machine-harvested group, including catechins, theaflavin, gallic acid, chlorogenic acid, and kaempferol-3-gluocside. Integrating sensory evaluation, OPLS-DA identified the six most important metabolites as significant contributors to sensory decline caused by harvesting mechanization. Furthermore, our research validates the possibility of using DD-SIMCA modelling with untargeted metabolomic data for distinguishing handpicked from machine-harvested tea products. The model was able to achieve 93% accuracy. This study provides crucial insights into the chemical and sensory shifts during mechanization, along with tools to manage and monitor these changes.
摘要：
机械化是茶叶收获的必然未来,但其对茶叶化学和品质的影响仍不确定。我们的研究基于UPLC-QToF-MS分析，检查了185种乌龙茶产品（铁观音）的非目标代谢组学数据，该产品是由手工或机器收获的叶子制成的。数据显示，机器收获组中一半以上的化学品损失至少50%，包括儿茶素,茶黄素,没食子酸,绿原酸,和山奈酚-3-葡萄糖苷。综合感官评价，OPLS-DA确定了六种最重要的代谢物是收获机械化引起的感官下降的重要原因。此外，我们的研究验证了使用DD-SIMCA模型与非目标代谢组学数据区分手工采摘和机器收获的茶产品的可能性.该模型能够达到93%的准确率。这项研究为机械化过程中的化学和感官变化提供了重要的见解，以及管理和监控这些变化的工具。
