Lactic acid bacteria: beyond fermentation to bio-protection against fungal spoilage and mycotoxins in food systems
Bibliographic record
Abstract
Recent outbreaks of foodborne diseases have highlighted the challenges of maintaining food safety, emphasizing the need for effective strategies to control pathogens and spoilage organisms. Toxins produced by indigenous fungi pose serious economic issues and undermine food security. Mycotoxin spoilage is a ubiquitous hazard that affects all food commodities; however, bakery products, dairy, fruits, vegetables, and meat are particularly vulnerable. The quality of food is perceived through senses such as taste, aroma, and texture. These sensory attributes significantly impact the overall sensation of the product and determine whether it will be accepted or rejected by consumers. Spoilage not only reduces consumer satisfaction but also drastically shortens the shelf life of food. This review highlights the ability of Lactic Acid Bacteria (LABs) to produce diverse antimicrobials, emphasizing antifungal metabolites as effective tools for enhancing food preservation and extending shelf life. As consumer demand for 'clean label' solutions increases, these natural antimicrobials promise safe and effective alternatives for enhancing food safety, reducing fungal spoilage, and extending shelf life of various perishable food commodities and reducing economic losses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".