HIGH‐PRESSURE DIFFERENTIAL SCANNING CALORIMETRY: EVALUATION OF PHASE TRANSITION IN PORK MUSCLE AT HIGH PRESSURES
Bibliographic record
Abstract
ABSTRACT High‐pressure (HP) differential scanning calorimetry (DSC) was used to investigate phase‐transition behavior of water in pork muscle at elevated pressures. Fresh pork (rib portion) muscle samples (0.62–0.72 g, vacuum‐packaged in polyethylene pouches) were tested through isothermal pressure‐scan (P‐scan, 0.3 MPa/min) and isobaric temperature‐scan (T‐scan, 0.15C/min) techniques. By using P‐scan testing procedure, the relationship between phase‐transition temperature (T) of frozen pork and the weighted‐average pressure during the phase‐change period (P̄1−2) was found to be T = −1.17 − 0.102P̄1−2 − 0.00019P̄ (R2 = 0.998, n = 10). Comparing with similar temperature pressure relationship classically established for pure water, it was observed that the depression of phase‐change temperature was much more pronounced in pork muscle than in pure water, and the degree of depression increased with an increase in pressure level. The ice content was evaluated with P‐scan at various constant calorimetric temperatures and compared with similar data from conventional DSC. Differences between the two were statistically insignificant (P > 0.05). T‐scan tests demonstrated phase‐transition behavior at constant pressure, but results were not very satisfactory. Overall, HP DSC seems a powerful technique for phase‐transition characterization of water in real foods during HP process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".