Control of pathogenic and spoilage bacteria in meat and meat products by high pressure: Challenges and future perspectives
Bibliographic record
Abstract
High-pressure processing is among the most widely used nonthermal intervention to reduce pathogenic and spoilage bacteria in meat and meat products. However, resistance of pathogenic bacteria strains in meats at the current maximum commercial equipment of 600 MPa questions the ability of inactivation by its application in meats. Pathogens including Escherichia coli, Listeria, and Salmonelle, and spoilage microbiota including lactic acid bacteria dominate in raw meat, ready-to-eat, and packaged meat products. Improved understanding on the mechanisms of the pressure resistance is needed for optimizing the conditions of pressure treatment to effectively decontaminate harmful bacteria. Effective control of the pressure-resistant pathogens and spoilage organisms in meats can be realized by the combination of high pressure with application of mild temperature and/or other hurdles including antimicrobial agents and/or competitive microbiota. This review summarized applications, mechanisms, and challenges of high pressure on meats from the perspective of microbiology, which are important for improving the understanding and optimizing the conditions of pressure treatment in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".