Evidence map›Paper›PMID 40870772›Full record

ArticleFoods (Basel, Switzerland)2025

Characterization and Exploration of the Flavor Profiles of Green Teas from Different Leaf Maturity Stages of

Xiaohui Liu, Mingzheng Huang, Weiyuan Tang, Yucai Li, Lun Li, Jinyi Xie, Xiangdong Li, Fabao Dong, Maosheng Wang

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. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Study on Quality Characteristics ofFoods (Basel, Switzerland) · 2026
    Article
  3. Article
  4. Article
  5. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Xiaohui LiuCollege of Food and Pharmaceutical Engineering, Guizhou Institute of Technology, Guiyang 550025, China.ORCID 0009-0004-2565-0824
Mingzheng HuangCollege of Food and Pharmaceutical Engineering, Guizhou Institute of Technology, Guiyang 550025, China.ORCID 0000-0002-3009-0559
Weiyuan TangCollege of Food and Pharmaceutical Engineering, Guizhou Institute of Technology, Guiyang 550025, China.
Yucai LiCollege of Food and Pharmaceutical Engineering, Guizhou Institute of Technology, Guiyang 550025, China.
Lun LiCollege of Food and Pharmaceutical Engineering, Guizhou Institute of Technology, Guiyang 550025, China.
Jinyi XieCollege of Food and Pharmaceutical Engineering, Guizhou Institute of Technology, Guiyang 550025, China.
Xiangdong LiCollege of Food and Pharmaceutical Engineering, Guizhou Institute of Technology, Guiyang 550025, China.
Fabao DongCollege of Food and Pharmaceutical Engineering, Guizhou Institute of Technology, Guiyang 550025, China.
Maosheng WangCollege of Food and Pharmaceutical Engineering, Guizhou Institute of Technology, Guiyang 550025, China.

Funding

Guizhou Fruit Wine Brewing Engineering Research Center [2022]050Guizhou Provincial Science and Technology Department KXJZ[2024]021the Guizhou Institute of Technology High-Level Talent Scientific Research Startup Project XJGC20190924the Science and Technology Plan Project of Guizhou province QKHJC-ZK[2022] YB180 and QKHJC MS[2025] 196the Science and Technology Plan Project of Guizhou province QKHZ[2023] YB064the Young Scientists Fund of the National Natural Science Foundation of China 32001463
6 · The paper itself

Abstract

Understanding how leaf maturity affects the flavor attributes of green tea is crucial for optimizing harvest timing and processing strategies. This study comprehensively characterized the flavor profiles of Fudingdabai green teas at three distinct leaf maturity stages-single bud (FDQSG), one bud + one leaf (FDMJ1G), and one bud + two leaves (FDTC2G)-using a multimodal approach integrating electronic nose, electronic tongue, HS-GC-IMS, relative odor activity value (rOAV) evaluation, and machine learning algorithms. A total of 85 volatile compounds (VOCs) were identified, of which 41 had rOAV > 1. Notably, 2-methylbutanal, 2-ethyl-3,5-dimethylpyrazine, and linalool exhibited extremely high rOAVs (>1000). FDQSG was enriched with LOX (lipoxygenase)-derived fresh, grassy volatiles such as (Z)-3-hexen-1-ol and nonanal. FDMJ1G showed a pronounced accumulation of floral and fruity compounds, especially linalool (rOAV: 7400), while FDTC2G featured Maillard- and phenylalanine-derived volatiles like benzene acetaldehyde and 2,5-dimethylfuran, contributing to roasted and cocoa-like aromas. KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis revealed significant enrichment in butanoate metabolism and monoterpenoid biosynthesis. Random forest-SHAP analysis identified 20 key flavor markers, mostly VOCs, that effectively discriminated samples by tenderness grade. ROC-AUC validation further confirmed their diagnostic performance (accuracy ≥ 0.8). These findings provide a scientific basis for flavor-driven harvest management and the quality-oriented grading of Fudingdaibai green tea.

Indexed as

flavor markersFudingdabaigreen teaHS-GC-IMSKEGG pathwayleaf tendernessmachine learningrOAVtea gradingvolatile compounds

Identifiers

PMID40870772
PMCPMC12385585

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LicenceCC BY
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Registered trials

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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.