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Effect of ultrasound assisted collagen peptide of chicken cartilage on storage quality of chicken breast meat

2022· article· en· W4294580032 on OpenAlexaff
Ye Zou, Liang Li, Jing Yang, Biao Yang, Jingjing Ma, Daoying Wang, Weimin Xu

Bibliographic record

VenueUltrasonics Sonochemistry · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsMinistry of Agriculture
FundersMinistry of Agriculture and Rural Affairs of the People's Republic of ChinaAgriculture Research System of ChinaJiangsu Academy of Agricultural SciencesMinistry of Agriculture of the People's Republic of ChinaNational Natural Science Foundation of ChinaJiangsu Agricultural Science and Technology Innovation Fund
KeywordsChicken breastChemistryFood scienceUltrasoundPeptideCartilageBiochemistryAnatomyBiologyMedicine

Abstract

fetched live from OpenAlex

This study investigated the effect of ultrasound assisted chicken cartilage collagen peptide (CP) treatment on the storage quality of chicken breast meat. There were five meat groups at 4 °C for 60 min as follows: untreatment (Control), immersing in deionized water (DW), ultrasound treatment in DW (UDW), immersing in CP (0.15 g/100 mL) solution and immersing in ultrasound combined with CP (UCP). The results showed that the drip and cooking loss of meat decreased significantly in UCP at4and -18 °Cwith the extension of storage time. A large amount of non-flowing water transformed into free water in the 4 °C for 5 d, and the smallest degree of water migration was observed at -18 °C in UCP. The texture parameters of UCP group were significantly improved, especially for decreased hardness and increased elasticity. Furthermore, there had no significant effect on the color of chicken breast.

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: Observational · Consensus signal: none
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 head0.021
GPT teacher head0.261
Teacher spread0.240 · 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 designObservational
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
Published2022
Admission routes1
Has abstractyes

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