The occurrence, growth, and biocontrol of <i>Listeria monocytogenes</i> in fresh and surface‐ripened soft and semisoft cheeses
Bibliographic record
Abstract
Listeria monocytogenes continues to pose a food safety risk in ready-to-eat foods, including fresh and soft/semisoft cheeses. Despite L. monocytogenes being detected regularly along the cheese production continuum, variations in cheese style and intrinsic/extrinsic factors throughout the production process (e.g., pH, water activity, and temperature) affect the potential for L. monocytogenes survival and growth. As novel preservation strategies against the growth of L. monocytogenes in susceptible cheeses, researchers have investigated the use of various biocontrol strategies, including bacteriocins and bacteriocin-producing cultures, bacteriophages, and competition with native microbiota. Bacteriocins produced by lactic acid bacteria (LAB) are of particular interest to the dairy industry since they are often effective against Gram-positive organisms such as L. monocytogenes, and because many LAB are granted Generally Regarded as Safe (GRAS) status by global food safety authorities. Similarly, bacteriophages are also considered a safe form of biocontrol since they have high specificity for their target bacterium. Both bacteriocins and bacteriophages have shown success in reducing L. monocytogenes populations in cheeses in the short term, but regrowth of surviving cells can commonly occur in the finished cheeses. Competition with native microbiota, not mediated by bacteriocin production, has also shown potential to inhibit the growth of L. monocytogenes in cheeses, but the mechanisms are still unclear. Here, we have reviewed the current knowledge on the growth of L. monocytogenes in fresh and surface-ripened soft and semisoft cheeses, as well as the various methods used for biocontrol of this common foodborne pathogen.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".