Effect of Pressure, Reconstituted RTE Meat Microbiota, and Antimicrobials on Survival and Post-pressure Growth of Listeria monocytogenes on Ham
Bibliographic record
Abstract
Pressure treatment of ready-to-eat (RTE) meats extends the shelf life and reduces risks associated with Listeria monocytogenes. However, pressure reduces numbers of Listeria on ham by less than 5 log (CFU/g) and pressure effects on other meat microbiota are poorly documented. This study aimed to investigate the impact of pressure and meat microbiota, with or without antimicrobials, on survival of Listeria after refrigerated storage. Ham was inoculated with a 5-strain cocktail of L. monocytogenes alone or together with a cocktail of competitive meat microbiota consisting of Brochothrix thermosphacta, Carnobacterium maltaromaticum, Leuconostoc gelidum, and Lactobacillus sakei. Products were treated at 500 MPa at 5°C for 1 or 3 min, with or without rosemary extract or nisin. Surviving cells were differentially enumerated after pressure treatment and after 4 weeks of refrigerated storage; meat microbiota after 4 weeks of storage were also analysed by high throughput sequencing of 16S rRNA amplicons. Pressure treatment of Listeria on ham for 1 or 3 min reduced counts by 1 and 2 log (CFU/g), respectively; inactivation of other meat microbiota was comparable. Counts of Listeria increased by 3 and 1 log (CFU/g) during refrigerated storage after 1 or 3 min of treatment, respectively. The presence of meat microbiota did not influence pressure inactivation of Listeria but prevented growth of Listeria during refrigerated storage. The addition of rosemary extract did not influence inactivation of Listeria or the meat microbiota, or growth of microorganisms during storage. The combination of nisin with pressure treatment for 3 min reduced counts of Listeria and meat microbiota by >5 log (CFU/g); after 4 weeks of storage, counts were below the detection limit. In conclusion, pressure application alone does not eliminate Listeria or meat microbiota on RTE ham; however, the presence of meat microbiota prevents growth of Listeria on pressure treated ham and has a decisive influence on post pressure survival and growth.
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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".