Listeria monocytogenes: A Continuous Global Threat in Ready-to-Eat (RTE) Foods
Bibliographic record
Abstract
Listeria monocytogenes is a significant foodborne pathogen associated with high rates of hospitalization and death, especially among vulnerable populations. Despite established regulatory standards and available antimicrobial intervention strategies, L. monocytogenes remains as a pathogen of concern in ready-to-eat (RTE) foods. This ultimately can lead to food recalls or listeriosis outbreak, highlighting its ongoing risks to food safety and public health. This review consolidates publicly accessible surveillance case counts and recall data of L. monocytogenes contamination from Australia, Europe, Canada, and the United States to assess the contamination trends in the RTE food supply chain. It also evaluates the effectiveness of antimicrobial intervention strategies, including both those currently implemented in industry and those that have been studied as potential interventions but are not yet widely adopted. Key factors affecting the efficiency of those strategies are identified, including food matrix composition, water activity (aw), fat content, and strain variability. Emerging multi-hurdle technology that integrates physical, chemical, and biological antimicrobial interventions are highlighted as promising approaches for maintaining both food safety and product quality. It also outlines the role of quantitative microbial risk assessment (QMRA) as a decision-support tool to select appropriate control strategies, predict recall risk and guide evidence-based risk management. Future research directions are proposed to expand the application of QMRA in managing recall risks throughout the RTE food supply chain due to L. monocytogenes.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".