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Record W4321371469 · doi:10.3390/foods12040877

Enhanced Gel Properties of Duck Myofibrillar Protein by Plasma-Activated Water: Through Mild Structure Modifications

2023· article· en· W4321371469 on OpenAlexaff
Wei Rao, M. S. Roopesh, Daodong Pan, Lihui Du

Bibliographic record

VenueFoods · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCircular dichroismChemistryDynamic mechanical analysisMyofibrilFluorescence spectroscopyRheologyAbsorption of waterChromatographyMicrostructureSpectroscopyBiophysicsFluorescenceCrystallographyPolymerOrganic chemistryBiochemistryMaterials science

Abstract

fetched live from OpenAlex

This study assessed the gel properties and conformational changes of duck myofibrillar protein (DMP) affected by plasma-activated water (PAW) generated at various discharge times (0 s, 10 s, 20 s, 30 s, and 40 s). With the treatment of PAW-20 s, the gel strength and water-holding capacity (WHC) of DMP gels were significantly increased when compared to the control group. Throughout the heating process, dynamic rheology revealed that the PAW-treated DMP had a higher storage modulus than the control. The hydrophobic interactions between protein molecules were significantly improved by PAW, resulting in a more ordered and homogeneous gel microstructure. The increased sulfhydryl and carbonyl content in DMP indicated a higher degree of protein oxidation with PAW treatment. Additionally, the circular dichroism spectroscopy demonstrated that PAW induced α-helix and β-turn transformed to β-sheet in DMP. Surface hydrophobicity, fluorescence spectroscopy, and UV absorption spectroscopy suggested that PAW altered DMP's tertiary structure, although the electrophoretic pattern indicated that the primary structure of DMP was mostly unaffected. These results suggest that PAW can improve the gel properties of DMP through mild alteration in its conformation.

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 score0.006
Threshold uncertainty score0.186

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 head0.069
GPT teacher head0.252
Teacher spread0.183 · 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

Citations18
Published2023
Admission routes1
Has abstractyes

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