Lactic Acid Bacteria in Dairy Foods: Prime Sources of Antimicrobial Compounds
Bibliographic record
Abstract
This review presents an in-depth examination of fermented dairy products, highlighting their significance as rich sources of antimicrobial agents. Through a comprehensive study of microbial activities during fermentation, we identify and discuss the rise of bioactive elements with antimicrobial characteristics. Bacteriocins such as nisin and pediocin play a significant role, as do organic acids such as lactic and acetic acid in providing antimicrobial activity. Challenges, including the enzymes, heat and pH sensitivity of certain compounds, are also touched upon, emphasizing the need for stable delivery for consistent efficacy. Our discussion covers various compounds, including bacteriocins, organic acids, and bioactive peptides, detailing their functions, action mechanisms, and potential applications. Moreover, this review discusses the emerging role of genetic engineering in optimizing lactic acid bacteria strains and exploring the potential of genetically modified organisms in improving the production and efficacy of antimicrobial compounds in dairy products. Additionally, we emphasize the interaction between beneficial microbes and their antimicrobial byproducts and discuss strategies for enhancing the synthesis of these antimicrobial compounds. The review highlights the nutritional significance of fermented dairy items and their potential as a rich source of compounds crucial for improving food safety. Additionally, the review explores challenges and potential solutions related to the stability of these compounds, ensuring their consistent efficacy and contribution to overall well-being.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".