Effect of Low-Temperature-High-Pressure Treatment on the Reduction of Escherichia coli in Milk
Bibliographic record
Abstract
Non-thermal processing of milk can potentially reduce nutrient loss, and a low-temperature-high-pressure (LTHP) treatment is considered as a promising alternative to thermal treatment, attracting considerable attention in recent years. The effect of LTHP treatment (−25 °C, 100–400 MPa) on the phase transition behavior of frozen milk was evaluated. The lethal and injured effects of different pressures and cycle numbers on E. coli in frozen milk were studied by using selective and non-selective enumeration media. Results from the gathered transient time–temperature–pressure data showed that pressures over 300 MPa could induce a phase transition from Ice I to Ice III. The treatment at −25 °C and 300 MPa could achieve a lethal effect similar to the two-cycle treatment of 400 MPa at room temperature. This meant that LTHP conditions can lower the operating pressure by at least 100 MPa or reduce the operation from two cycle to one cycle. Increasing the number of pressure cycles enhanced the lethal effects, which was not additive, but resulted in a transformation of part of the injured cells into dead cells. Transmission electron microscopy (TEM) and scanning electron microscopy (SEM) provided direct evidence for the breakdown of cell membrane and cell walls by phase transitions. Combined with a designed internal cooling device, the LTHP process can be expected to be a more attractive alternative to non-thermal processing for the dairy industry.
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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".