Evidence map›Paper›PMID 40547006›Full record

ArticleFood chemistry: X2025

Characterization of volatile profiles in cherry fruits: Integration of E-nose and HS-SPME-GC-MS.

Chen Feng, Xiaoming Zhang, Jing Wang, Chuanbao Wu, Wei Wang, Guohua Yan, Yu Zhou, Kaichun Zhang, Xuwei Duan

Abstract read
In one paragraph

Article in Food chemistry: X, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Flavor and metabolite responses ofFood chemistry: X · 2026
    Article
  5. Comparative volatile profiling ofFrontiers in plant science · 2026
    Article
  6. Article
  7. Sensory-Chemical Co-Dynamics inFoods (Basel, Switzerland) · 2025
    Article
  8. Article
  9. Article
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.

Chen FengInstitute of Forestry and Pomology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100093, China.
Xiaoming ZhangInstitute of Forestry and Pomology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100093, China.
Jing WangInstitute of Forestry and Pomology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100093, China.
Chuanbao WuInstitute of Forestry and Pomology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100093, China.
Wei WangInstitute of Forestry and Pomology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100093, China.
Guohua YanInstitute of Forestry and Pomology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100093, China.
Yu ZhouInstitute of Forestry and Pomology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100093, China.
Kaichun ZhangInstitute of Forestry and Pomology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100093, China.
Xuwei DuanInstitute of Forestry and Pomology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100093, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aroma plays a crucial role in assessing the flavor quality of cherry fruits. This study compared the odor and volatile profiles of 37 cherry germplasm fruits using an electronic nose (E-nose) combined with headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS). Notably, Cao5 exhibited a distinct odor compared to the other 36 sweet cherry cultivars. A total of 363 volatiles were identified and categorized into 14 classes. In terms of quantity, the top three categories of volatiles were terpenoids, esters, and alcohols. Among the samples, Saylor displayed the highest total volatile content at 76.98 μg∙g

Indexed as

CherryE-noseHS-SPME-GC–MSRelative odor activity valueVolatile compounds

Identifiers

PMID40547006
PMCPMC12180960

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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.