Evidence map›Paper›PMID 35874543›Full record

ArticleFrontiers in physiology2022

A Comparison of Different Tissues Identifies the Main Precursors of Volatile Substances in Chicken Meat.

Na Luo, Li Liu, Xiaoya Yuan, Yuxi Jin, Guiping Zhao, Jie Wen, Huanxian Cui

Open access · goldAbstract read
In one paragraph

Article in Frontiers in physiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.0field-weighted citation impact, top 29% of its field
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

8 citing papers in PubMed, 14 citations in OpenAlex.

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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

7 authors at 2 institutions in 1 country.

Na LuoState Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China.
Li LiuState Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China.
Xiaoya YuanState Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China.
Yuxi JinState Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China.
Guiping ZhaoState Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China.
Jie WenState Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China.
Huanxian CuiState Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China.
Institute of Animal Sciences · CNChinese Academy of Agricultural Sciences · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Amino acids and fatty acids are the main precursors of volatile organic compounds (VOCs) in meat. The purpose of this study was to determine the main VOC components in chicken breast muscle (BM) and abdominal fat (AF) tissue, as well as the source of VOCs, to provide a basis for quality improvement of broilers. BM and AF served as experimental and control groups, and gas chromatography-mass spectrometry (GC-MS) and untargeted metabolomics were employed to identify the source of VOCs. The results revealed nine VOCs in BM and AF tissues, including hexanal, octanal, and nonanal. VOCs including 1-octen-3-ol, (E,E)-2, 4-nonadienal, and benzaldehyde were significantly elevated in BM compared with AF (

Indexed as

abdominal fataroma precursorbreast musclechickenmeat

Identifiers

PMID35874543
PMCPMC9301024
OpenAlexW4284710098

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.