HIGH‐PRESSURE DIFFERENTIAL SCANNING CALORIMETRY (DSC): EQUIPMENT AND TECHNIQUE VALIDATION USING WATER–ICE PHASE‐TRANSITION DATA
Bibliographic record
Abstract
ABSTRACT Understanding phase transition during high‐pressure (HP) processing of foods is important both with respect to optimizing the process and improvement of product quality, but scientific information available in this area is very limited. In this study, the phase‐transition behavior of water was evaluated using a HP differential scanning calorimetry (DSC). Tests were carried out under both isothermal pressure‐scan (P‐scan) and isobaric temperature‐scan (T‐scan) modes with distilled water prefrozen in the sample cell. P‐scan was carried out at 0.3 MPa/min at two temperatures, −10 and −20C, and T‐scan was carried out at 0.15C/min at two pressures, 0.1 and 115 MPa. The pressure‐induced phase transition of water was accurately reproduced by the P‐scan test. Ice melting latent heat during P‐scan showed no significant difference (P > 0.05) from the available reference data in literature. The relationship between P‐scan tested (Lm) and reference latent heat was Lm = 0.987 L (R2 = 0.99, n = 6) suggesting a mean error less than 2%. T‐scan mode was less appropriate and did not yield promising result. Measured values were less accurate than P‐scan probably because of the influence of large heat capacity of sample cell. However, reliable and reproducible results obtained under P‐scan mode suggested that the HP DSC can be used for the calorimetric determination of pressure‐dependent water‐phase transition in real food systems during HP freezing/thawing operations.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".