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Record W3049239710 · doi:10.1111/1750-3841.15370

Effect of protein oxidation and degradation on texture deterioration of ready‐to‐eat shrimps during storage

2020· article· en· W3049239710 on OpenAlexaff
Deyang Li, Ziqiang Liu, Bing Liu, Qi Yan, Yuxin Liu, Xiaoyang Liu, Lei Qin, Dayong Zhou, Fereidoon Shahidi

Bibliographic record

VenueJournal of Food Science · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsMemorial University of Newfoundland
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsChewinessMyofibrilChemistryProtein degradationDegradation (telecommunications)Texture (cosmology)Trichloroacetic acidFood sciencePeptideMicrostructureStorage proteinFragmentation (computing)BiochemistryCrystallographyBiology

Abstract

fetched live from OpenAlex

The impact of protein oxidation and degradation on texture deterioration of ready-to-eat (RTE) shrimps during storage was investigated. The deterioration in texture during storage was manifested by decreased instrumental hardness, elasticity, chewiness, and recoverability. The occurrence of protein oxidation was revealed by a significant increase in the contents of free radicals and carbonyls. The increases in trichloroacetic acid-soluble peptide (TCA-soluble peptide) content and myofibril fragmentation index (MFI) were also observed, suggesting the degradation of protein. Pearson correlation analysis showed that the decreased instrumental texture parameters were negatively correlated with the increased carbonyl content, TCA-soluble peptide, MFI, porosity, and pore size as well as the decreased water-holding capacity (WHC), thus, it was hypothesized that protein oxidation and degradation were responsible for changes in the microstructure and reduction of WHC, which ultimately resulted in texture deterioration of RTE shrimps.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.261
Teacher spread0.228 · 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 source (direct Gemma or distilled Codex), 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

Citations36
Published2020
Admission routes1
Has abstractyes

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