HIGH‐PRESSURE DESTRUCTION KINETICS OF SPOILAGE AND PATHOGENIC BACTERIA IN RAW MILK CHEESE
Bibliographic record
Abstract
ABSTRACT Raw milk cheese samples were individually inoculated with Escherichia coli K‐12, Escherichia coli O157:H7 and Listeria monocytogenes, and subjected to high‐pressure (HP) treatments (200–400 MPa) in both pressure hold and pressure pulse modes. Pressure inactivation of E. coli K‐12 at 350 MPa (5 min) at different temperatures demonstrated a strong temperature effect when temperature was 40C and above. Pressure destruction kinetics and pressure sensitivity were evaluated at 25C (where the temperature effect was minimal). HP treatment generally demonstrated a step‐change pressure pulse effect followed by a holding time destruction that was well described by a first‐order model (R2 > 0.90) with higher pressures resulting in a faster rate of microbial reduction (smaller D values). The associated D values at an intermediate pressure of 300 MPa were 4.4, 14.5 and 3.6 min for E. coli K‐12, E. coli O157:H7 and L. monocytogenes, respectively, with E. coli O157:H7 thus demonstrating a higher‐pressure resistance than the other two. The corresponding pressure zp values were 156, 128 and 82 MPa, and ΔV≠(Arrhenius volume change of activation) values were −3.6, −4.4 and −7.0 (×10−5 m3/mole), respectively. Pulse mode treatments showed similar pressure resistance trends to pressure hold mode, but were not effective for E. coli O157:H7.
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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".