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Record W4393985951 · doi:10.1155/2024/6696954

Application of the Effect of Nonthermal Technologies on the Oxidation of Proteins and Lipids in Pigeon Meat during Chilled Storage

2024· article· en· W4393985951 on OpenAlexaff
Xiaoyang Tong, Yi Zhang, Jinxin Pang, Baolin Liu, Éric Biron, Md Ramim Tanver Rahman, Yongjin Qiao, Qi-Jie Bing, Xu-Ying Gao

Bibliographic record

VenueJournal of Food Biochemistry · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsFood scienceLipid oxidationChemistryFood storageBiochemistry

Abstract

fetched live from OpenAlex

This study investigated the effects of low-voltage electrostatic field (LVEF), electron beam irradiation (EBI), and modified atmosphere packaging (MAP) on protein and lipid oxidation of pigeon meat (PM) during chilled storage. The water-holding capacity (WHC) and color reflected that the difference between the LVEF-treated (1.20 kV/m) group and the fresh groups was the smallest. The oxidation analysis of protein showed that the LVEF-treated group had smaller values of carbonyl concentration (0.0551 ± 0.0048 μmol/g), larger values of sulfhydryl concentration (0.737 ± 0.0364 μmol/g), and higher Ca2+-ATPase activity (30.10 ± 1.52 U/g) than the C4 group (0.0649 ± 0.0013 μmol/g, 0.510 ± 0.0225 μmol/g, and 25.18 ± 1.42 U/g, respectively). SDS-PAGE demonstrated the ability of LVEF to inhibit the hydrolysis and cross-linking of proteins. Meanwhile, the LVEF-treated group had lower values of TBARs (2.26 ± 0.0371 μg/kg) than the other groups and had a lower lipid oxidation level. In addition, the fatty acids in the LVEF-treated group were similar to those in the fresh group and were beneficial to human health. In conclusion, LVEF (1.20 kV/m) could inhibit protein and lipid oxidation of PM during chilled storage.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.066

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.208
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2024
Admission routes1
Has abstractyes

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