Unravelling the Potential of Lactococcus lactis Strains to Be Used in Cheesemaking Production as Biocontrol Agents
Bibliographic record
Abstract
This research, developed within an exchange program between Italy and Canada, represents the first step of a three-year project intended to evaluate the potential of nisin-producing Lactococcus lactis strains isolated from Italian and Canadian dairy products to select a consortium of strains to be used as biocontrol agents in Crescenza and Cheddar cheese production. In this framework, the acidification and the production of nisin in milk, and the volatile molecule profiles of the fermented milk, were recorded. The strains were further tested for their anti-Listeria monocytogenes activity in milk. The data obtained highlighted good potential for some of the tested strains, which showed production of nisin beginning within 12 h after the inoculation and reaching maximum levels between 24 and 48 h. The highest inactivation levels of L. monocytogenes in milk was reached in the presence of the strains 101877/1, LBG2, 9FS16, 11FS16, 3LC39, FBG1P, UL36, UL720, UL35. The strains generated in milk-specific volatile profiles and differences in the presence of fundamental aromatic molecules of dairy products, such as 2-butanone and diacetyl. The results highlight the interesting potential of some L. lactis strains, the producer of nisin, to be further used as biocontrol agents, although the strains need to be tested for interaction with traditional thermophilic starters and tested in real cheesemaking conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".