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Record W2561074111 · doi:10.1111/ijfs.13327

High‐pressure processing‐induced conformational changes during heating affect water holding capacity of myosin gel

2016· article· en· W2561074111 on OpenAlexaff
Mengyao Wang, Xing Chen, Yufeng Zou, Hongqiang Chen, Siwen Xue, Qian Chang, Peng Wang, Xinglian Xu, Guanghong Zhou

Bibliographic record

VenueInternational Journal of Food Science & Technology · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsMinistry of Education and Child Care
FundersPriority Academic Program Development of Jiangsu Higher Education InstitutionsNational Natural Science Foundation of China
KeywordsMyosinPascalizationChemistryWater holding capacityHomogeneousBiophysicsDenaturation (fissile materials)Thermal treatmentChromatographyHigh pressureBiochemistryMaterials scienceFood scienceThermodynamicsNuclear chemistryComposite materialBiology

Abstract

fetched live from OpenAlex

Summary This study aimed to investigate the effects of high‐pressure processing ( HPP ) (0.1‐400 MP a for 9 min) on the water holding capacity ( WHC ) of heat‐induced rabbit myosin gel and structural changes during thermal treatment (25–75 °C). HPP at 100 MP a significantly increased the WHC ( P < 0.05) and formed more regular and homogeneous three‐dimensional network. Myosin tails at 100 MP a unfolded completely during the thermal treatment, which was beneficial to form a high WHC gel network. However, myosin pressurised at 200 MP a and above formed a weak gel. Their heads were already aggregated before heating, preventing from subsequent thermal denaturation and aggregation. With the temperature increasing, unfolding of myosin tails was not sufficient for a filamentous network formation. These results suggested that HPP could modify the myosin structure and affect the gel formation during heating. The 100 MP a was the optimum pressure level for the WHC of rabbit myosin gel.

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)0.001
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 score0.020
Threshold uncertainty score0.172

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.042
GPT teacher head0.268
Teacher spread0.226 · 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

Citations37
Published2016
Admission routes1
Has abstractyes

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