High Pressure Sub-Zero Temperature Concepts for Improving Microbial Safety and Maintaining Food Quality: Background Fundamentals, Equipment Issues and Applications
Bibliographic record
Abstract
Ensuring food safety and quality is paramount for consumers. Water, a fundamental component of food, exhibits diverse crystalline structures under high pressure (HP) and experiences alterations in freezing points. Exploiting these water characteristics has spurred the development of innovative high-pressure sub-zero temperature (HPST) techniques. This review systematically categorizes HPST technologies based on their adherence to the temperature-pressure phase diagram, encompassing high-pressure freezing (HPF), high-pressure thawing (HPT), solid-solid phase transition (SSPT) treatment, and isochoric freezing (IF). Furthermore, the review elaborates on the latest advancements in HP cryogenic equipment, focusing on temperature and pressure control units. The underlying principles of the HPST technologies and their inactivation effects on various food materials, such as beverages and meat, are elaborated, and the effects of the HPST treatments on the appearance, texture, moisture content, and other quality characteristics of the food products are discussed. Emphasis is placed on delineating the advantages, disadvantages, and application scopes of different HPST technologies. To propel comprehensive research and foster the industrialization of HPST technology, future endeavors should concentrate on validating the commercial viability of HP cryogenic equipment, establishing dependable temperature regulation and detection systems, and compiling a comprehensive melting point database for real food under HP conditions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".