Examination of the Use of Bacteriophage as an Additive and Determining Its Best Application Method to Control Listeria monocytogenes in a Cooked-Meat Model System
Bibliographic record
Abstract
The study examined the efficacy of using bacteriophage as an additive in a cooked-meat model system to control growth of contaminating Listeria monocytogenes during subsequent storage. Studies were designed where Listeria bacteriophage A511 and L. monocytogenes introduced inside or on the surface of the cooked meat to simulate different bacteriophage application and pathogen contamination scenarios. These scenarios include; 1) A511 and L. monocytogenes in meat; 2) A511 in meat, L. monocytogenes on surface; 3) L. monocytogenes in meat, A511 on surface; and 4) L. monocytogenes followed by A511 on meat surface. Real world bacteriophage application and pathogen contamination levels of 109 PFU/g and 103-4 CFU/g, respectively, were used. These meats were then vacuum packaged and stored at 4 C and changes in A511 titers and L. monocytogenes numbers were enumerated during the 28 day storage. Under the conditions tested, application of A511 directly on top of L. monocytogenes contaminating the surface of the meat was the only scenario where L. monocytogenes numbers were reduced to below detection limits and remained significantly lower than the controls for up to 20 days. Although A511 titers remained stable when applied as an additive in meat, they were not successful in controlling growth of the contaminating L. monocytogenes (present inside or on surface of meat). Similarly, application of A511 on the surface of the meat could not control growth of L. monocytogenes present inside the meat. Listeria monocytogenes numbers increased from the initial 3-log CFU/g to 9-log CFU/g similar to the controls by the end of the 28 day storage. These results suggest that bacteriophages are effective in controlling growth of surface contaminating bacteria only when applied directly onto the surface of the contaminated food product, and are ineffective as a biocontrol agent when used as an additive.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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 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".