Evidence map›Paper›PMID 38974721›Full record

ArticleFood science of animal resources2024

Volatile Compounds for Discrimination between Beef, Pork, and Their Admixture Using Solid-Phase-Microextraction-Gas Chromatography-Mass Spectrometry (SPME-GC-MS) and Chemometrics Analysis.

Zubayed Ahamed, Jin-Kyu Seo, Jeong-Uk Eom, Han-Sul Yang

Abstract read
In one paragraph

Article in Food science of animal resources, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

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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

4 authors.

Zubayed AhamedDivision of Applied Life Science (BK21Four), Gyeongsang National University, Jinju 52828, Korea.ORCID 0000-0002-9947-2299
Jin-Kyu SeoDivision of Applied Life Science (BK21Four), Gyeongsang National University, Jinju 52828, Korea.ORCID 0000-0001-5929-8284
Jeong-Uk EomDivision of Applied Life Science (BK21Four), Gyeongsang National University, Jinju 52828, Korea.ORCID 0000-0003-1856-7745
Han-Sul YangDivision of Applied Life Science (BK21Four), Gyeongsang National University, Jinju 52828, Korea.ORCID 0000-0001-6658-6364

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study addresses the prevalent issue of meat species authentication and adulteration through a chemometrics-based approach, crucial for upholding public health and ensuring a fair marketplace. Volatile compounds were extracted and analyzed using headspace-solid-phase-microextraction-gas chromatography-mass spectrometry. Adulterated meat samples were effectively identified through principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA). Through variable importance in projection scores and a Random Forest test, 11 key compounds, including nonanal, octanal, hexadecanal, benzaldehyde, 1-octanol, hexanoic acid, heptanoic acid, octanoic acid, and 2-acetylpyrrole for beef, and hexanal and 1-octen-3-ol for pork, were robustly identified as biomarkers. These compounds exhibited a discernible trend in adulterated samples based on adulteration ratios, evident in a heatmap. Notably, lipid degradation compounds strongly influenced meat discrimination. PCA and PLS-DA yielded significant sample separation, with the first two components capturing 80% and 72.1% of total variance, respectively. This technique could be a reliable method for detecting meat adulteration in cooked meat.

Indexed as

adulterationcooked meatpartial least square-discriminant analysis (PLS-DA)principal component analysis (PCA)solid-phase-microextraction-gas chromatography-mass spectrometry (SPME-GC-MS)

Identifiers

PMID38974721
PMCPMC11222689

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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.