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Effects of ultrasound-assisted tumbling on the quality and protein oxidative modification of spiced beef

2025· article· en· W4407722747 on OpenAlexaff
Wenxuan Wang, Feiyan Jiang, Lujuan Xing, Yan Huang, Wangang Zhang

Bibliographic record

VenueUltrasonics Sonochemistry · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsMinistry of Education and Child Care
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsOxidative phosphorylationFood scienceUltrasoundChemistryBiochemistryPhysicsAcoustics

Abstract

fetched live from OpenAlex

The aim of this study was to investigate the effects of ultrasound-assisted tumbling (UT) with different ultrasound powers (frequency 20 kHz, powers of 0, 300 W, 450 W and 600 W) on the quality of spiced beef as explained from the perspective of the changes of muscle fibers and myofibrillar proteins (MPs). The results showed that pH value, tenderness and yield rate of UT groups were all apparently improved compared with the single tumbling group (P < 0.05). Ultrasound-assisted tumbling treatment could loosen muscle fiber structure supported by scanning electron microscopy (SEM) result, and the increased myofibrillar fragmentation index (MFI) value (P < 0.05). Additionally, an upward trend was observed in protein oxidation degree with the rise of ultrasound power level (P < 0.05), while the difference between groups in MPs solubility was not significant (P > 0.05). Above all, ultrasound-assisted tumbling treatment could effectively improve the quality of spiced beef by exacerbating the modifications in muscle fiber structure and MPs.

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)0.000
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 score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.042
GPT teacher head0.292
Teacher spread0.250 · 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

Citations13
Published2025
Admission routes1
Has abstractyes

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