Decoding Light-Spreading Intensity Effects on the Sensory Quality and Volatile Compounds of Green Tea: An Integrated GC-E-Nose and Targeted Metabolomics Analysis.

Qiwei Wang, Jiajing Hu, Jiahao Tang, Xianxiu Zhou, Haibo Yuan, Yongwen Jiang, Jialing Xie, Yanqin Yang

Abstract read

In one paragraph

Article in Foods (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Qiwei WangNational Key Laboratory for Tea Plant Germplasm Innovation and Resource Utilization, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China.ORCID 0009-0003-8927-3522

Jiajing HuNational Key Laboratory for Tea Plant Germplasm Innovation and Resource Utilization, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China.

Jiahao TangNational Key Laboratory for Tea Plant Germplasm Innovation and Resource Utilization, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China.

Xianxiu ZhouNational Key Laboratory for Tea Plant Germplasm Innovation and Resource Utilization, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China.

Haibo YuanNational Key Laboratory for Tea Plant Germplasm Innovation and Resource Utilization, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China.

Yongwen JiangNational Key Laboratory for Tea Plant Germplasm Innovation and Resource Utilization, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China.

Jialing XieHezhou Agriculture and Rural Affairs Bureau, Hezhou 542800, China.

Yanqin YangNational Key Laboratory for Tea Plant Germplasm Innovation and Resource Utilization, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China.ORCID 0000-0001-8793-9157

Funding

the Science and Technology Innovation Project of the Chinese Academy of Agricultural Sciences CAAS-ASTIP-TRICAAS

6 · The paper itself

Abstract

Spreading, the preliminary step in the production of green tea, is crucial for achieving superior tea quality. This study investigated the effects of spreading on the sensory quality and volatile compounds in green tea under varying intensities of yellow light, employing GC-E-Nose and targeted metabolomics. A notable improvement in overall sensory quality was noted in tea samples subjected to a higher intensity of 6000 Lux, which was characterized by a delightful floral fragrance. In total, 70 volatile compounds were successfully identified, with 61 volatiles detected across all five light intensities. Moreover, 21 pivotal odorants featuring odor activity value (OAV) levels higher than one were determined, among which

Indexed as

GC-E-NoseGC-MS/MSgreen tealight intensityvolatile compounds

Identifiers

PMID40282715

DOI10.3390/foods14081313

PMCPMC12027006

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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.