Evidence map›Paper›PMID 38928758›Full record

ArticleFoods (Basel, Switzerland)2024

Combined Relaxation Spectra for the Prediction of Meat Quality: A Case Study on Broiler Breast Fillets with the Wooden Breast Condition.

Bin Pang, Brian Bowker, Seung-Chul Yoon, Yi Yang, Jian Zhang, Changhu Xue, Yaoguang Chang, Jingxin Sun, Hong Zhuang

Abstract read
In one paragraph

Article in Foods (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Bin PangCollege of Food Science & Engineering, Qingdao Agricultural University, Qingdao 266109, China.ORCID 0000-0003-4027-0227
Brian BowkerU.S. National Poultry Research Center, USDA-Agricultural Research Service, Athens, GA 30605, USA.
Seung-Chul YoonU.S. National Poultry Research Center, USDA-Agricultural Research Service, Athens, GA 30605, USA.ORCID 0000-0001-8143-6559
Yi YangSchool of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China.
Jian ZhangInstitute of Animal Husbandry and Veterinary Medicine, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.ORCID 0000-0002-9885-6020
Changhu XueCollege of Food Science & Engineering, Ocean University of China, Qingdao 266003, China.
Yaoguang ChangCollege of Food Science & Engineering, Ocean University of China, Qingdao 266003, China.
Jingxin SunCollege of Food Science & Engineering, Qingdao Agricultural University, Qingdao 266109, China.
Hong ZhuangU.S. National Poultry Research Center, USDA-Agricultural Research Service, Athens, GA 30605, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study evaluated the potential of using combined relaxation (CRelax) spectra within time-domain nuclear magnetic resonance (TD-NMR) measurements to predict meat quality. Broiler fillets affected by different severities of the wooden breast (WB) conditions were used as case-study samples because of the broader ranges of meat-quality variations. Partial least squares regression (PLSR) models were established to predict water-holding capacity (WHC) and meat texture, demonstrating superior CRelax capabilities for predicting meat quality. Additionally, a partial least squares discriminant analysis (PLS-DA) model was developed to predict WB severity based on CRelax spectra. The models exhibited high accuracy in distinguishing normal fillets from those affected by the WB condition and demonstrated competitive performance in classifying WB severity. This research contributes innovative insights into advanced spectroscopic techniques for comprehensive meat-quality evaluation, with implications for enhancing precision in meat applications.

Indexed as

chemometricsmeat-quality assessmentmeat textureNMRwater-holding capacitywoody breast myopathy

Identifiers

PMID38928758
PMCPMC11202802

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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.