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Record W4385381938 · doi:10.1111/ijfs.16620

Recent advances in the effects of protein oxidation on aquatic products quality: mechanism and regulation

2023· article· en· W4385381938 on OpenAlexaff
Ye Yu, Yongqiang Zhao, Yi Zhang, Benjamin K. Simpson, Shengjun Chen, Yueqi Wang, Chunsheng Li, Di Wang, Chuang Pan

Bibliographic record

VenueInternational Journal of Food Science & Technology · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsMcGill University
FundersCentral Public-interest Scientific Institution Basal Research Fund, Chinese Academy of Fishery Sciences
KeywordsAquatic ecosystemBiochemical engineeringAquatic environmentQuality (philosophy)Mechanism (biology)Protein qualityBusinessBiologyChemistryFood scienceEnvironmental chemistryEcologyEngineering

Abstract

fetched live from OpenAlex

Summary Aquatic products are delicious and rich in high‐quality proteins, and thus have high‐food value and are popular among consumers; however, deterioration of their quality can easily occur when being stored and processed. Proteins, the main components of the muscle tissue of aquatic products, are closely related to the quality of aquatic products, and protein oxidation has become a research hotspot for studying the regulatory mechanisms of aquatic product quality changes. Protein oxidation is one of the main causes of quality deterioration of aquatic products, mainly because of protein cross‐linking and protein degradation caused by oxidation. Related studies have also shown that the changes in the electrostatic charge of proteins owing to oxidation are associated with the muscle water‐holding capacity (WHC). This article introduces the induction mechanism of protein oxidation in aquatic products, focusing on its effects on the quality, such as colour, texture, WHC and summarises the current research progress of aquatic products quality control on this basis to provide theoretical reference for future research.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.158
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.034
GPT teacher head0.302
Teacher spread0.269 · 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

Citations30
Published2023
Admission routes1
Has abstractyes

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