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High Hydrostatic Pressure Effects on the Texture of Meat and Meat Products

2009· article· en· W2038744481 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Food Science · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsChewinessHydrostatic pressureCooked meatChemistryFood scienceTexture (cosmology)Denaturation (fissile materials)BreakageMuscle proteinHigh pressureBiophysicsMaterials scienceSkeletal muscleComposite materialAnatomyBiologyThermodynamics

Abstract

fetched live from OpenAlex

High hydrostatic pressure (HHP) treatment can influence meat protein conformation and induce protein denaturation, aggregation, or gelation. The means whereby HHP treatment exerts effects on meat protein structure change are due to the rupture of noncovalent interactions within protein molecules, and to the subsequent re-formation of intra- and inter-molecular bonds within or among protein molecules. Depending upon the meat protein system, the pressure, the temperature, and the duration of the pressure treatment, meat can be either tenderized or toughened. Muscle texture variation induced by heat treatment is due to breakage of hydrogen bonds, whereas changes from high pressure treatment are due to the rupture of hydrophobic and electrostatic interactions. Pressure treatment has little effect on the toughness of connective tissue. Juiciness, springiness, and chewiness are increased upon HHP treatment. Prerigor HHP treatment tenderizes meat, whereas tenderizing effects of postrigor HHP treatment are only measureable if pressure and heat treatment are combined. The limitations and future applications of high pressure technology are also discussed.

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.

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)0.001
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 score0.222
Threshold uncertainty score0.160

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.000
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 head0.026
GPT teacher head0.239
Teacher spread0.214 · 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