Impact of lactic acid bacteria on the control of <i>Listeria monocytogenes</i> in ready-to-eat foods
Bibliographic record
Abstract
Abstract Due to the increased demand for ready-to-eat (RTE) minimally processed foods, alternatives to chemical and thermal preservation methods to maintain food safety are highly demanded. A significant safety hazard in RTE food products is the growth of the foodborne pathogen Listeria monocytogenes (L. monocytogenes). After processing, recontamination or cross-contamination of L. monocytogenes in RTE food products may occur and the lack of cooking can lead to an increased risk of listeriosis. Further, some RTE food products (e.g. cheese and cured meat) can have a long processing period and shelf life, thus allowing for the growth and proliferation of L. monocytogenes in the food matrix. Lactic acid bacteria (LAB) are generally recognized as safe probiotics and have been proposed as a biological control approach to eliminate foodborne pathogens including L. monocytogenes. LAB have been reported to extend the shelf life of food products and inhibit pathogen proliferation via growth competition and metabolite production. LAB are native microflora of many RTE foods, but only certain LAB may inhibit pathogen growth. Therefore, the specificity of LAB species should be employed in their use in RTE foods. This review will discuss the antimicrobial mechanisms of LAB against L. monocytogenes, selective use of LAB in food matrices, and their uses in food processing and packaging.
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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 | 0.001 | 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".