Evidence map›Paper›PMID 35310568›Full record

ArticleFood science of animal resources2022

Distinguishing Aroma Profile of Highly-Marbled Beef according to Quality Grade using Electronic Nose Sensors Data and Chemometrics Approach.

Dicky Tri Utama, Aera Jang, Gur Yoo Kim, Sun-Moon Kang, Sung Ki Lee

Open access · diamondAbstract read
In one paragraph

Article in Food science of animal resources, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.8field-weighted citation impact, top 18% 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

6 citing papers in PubMed, 13 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

5 authors at 2 institutions in 2 countries.

Dicky Tri UtamaDepartment of Applied Animal Science, College of Animal Life Sciences, Kangwon National University, Chuncheon 24341, Korea.ORCID https://orcid.org/0000-0003-2344-8548
Aera JangDepartment of Applied Animal Science, College of Animal Life Sciences, Kangwon National University, Chuncheon 24341, Korea.ORCID https://orcid.org/0000-0003-1789-8956
Gur Yoo KimDepartment of Applied Animal Science, College of Animal Life Sciences, Kangwon National University, Chuncheon 24341, Korea.ORCID https://orcid.org/0000-0001-9973-3367
Sun-Moon KangDepartment of Animal Products Development and Utilization, National Institute of Animal Science, Rural Development Administration, Wanju 55365, Korea.ORCID https://orcid.org/0000-0003-3947-4337
Sung Ki LeeDepartment of Applied Animal Science, College of Animal Life Sciences, Kangwon National University, Chuncheon 24341, Korea.ORCID https://orcid.org/0000-0002-2989-4787
Kangwon National University · KRRural Development Administration · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fat deposition in animal muscles differs according to the genetics and muscle anatomical locations. Moreover, different fat to lean muscle ratios (quality grade, QG) might contribute to aroma development in highly marbled beef. Scientific evidence is required to determine whether the abundance of aroma volatiles is positively correlated with the amount of fat in highly marbled beef. Therefore, this study aims to investigate the effect of QG on beef aroma profile using electronic nose data and a chemometric approach. An electronic nose with metal oxide semiconductors was used, and discrimination was performed using multivariate analysis, including principal component analysis and hierarchical clustering. The

Indexed as

hierarchical clusteringlipid oxidationmarblingmultivariate analysisprincipal component analysisvolatile compounds

Identifiers

PMID35310568
PMCPMC8907795
OpenAlexW4205302634

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.