The Impact of Harvesting Mechanization on Oolong Tea Quality.

Junling Zhou, Shuilian Gao, Zhenghua Du, Tongda Xu, Chao Zheng, Ying Liu

Abstract read

In one paragraph

Article in Plants (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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5 · Who and what money

Authors and funding

6 authors.

Junling ZhouCollege of Horticulture, Haixia Institute of Science and Technology, Fujian Agriculture and Forestry University, Fuzhou 350007, China.

Shuilian GaoCollege of Horticulture, Haixia Institute of Science and Technology, Fujian Agriculture and Forestry University, Fuzhou 350007, China.

Zhenghua DuCollege of Horticulture, Haixia Institute of Science and Technology, Fujian Agriculture and Forestry University, Fuzhou 350007, China.

Tongda XuCollege of Horticulture, Haixia Institute of Science and Technology, Fujian Agriculture and Forestry University, Fuzhou 350007, China.

Chao ZhengCollege of Horticulture, Haixia Institute of Science and Technology, Fujian Agriculture and Forestry University, Fuzhou 350007, China.

Ying LiuCollege of Horticulture, Haixia Institute of Science and Technology, Fujian Agriculture and Forestry University, Fuzhou 350007, China.ORCID 0000-0002-1403-7803

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

chemometricharvesting mechanizationoolong teaquality controlsensoryuntargeted metabolomicsUPLS-QToF-MS

Identifiers

PMID38498582

DOI10.3390/plants13040552

PMCPMC10892732

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.