Microbial strategies to control aflatoxins in food and feed
Bibliographic record
Abstract
Aflatoxins are a group of toxic and carcinogenic fungal metabolites. They are commonly found in cereals, nuts and animal feeds and create a significant threat to the food industry and animal production. Several strategies have been developed to avoid or reduce harmful effects of aflatoxins since the 1960s. However, prevention of aflatoxin contamination pre/post harvest or during storage has not been satisfactory and control strategies such as physical removing and chemical inactivating used in food commodities have their deficiencies, which limit their large scale application. It is expected that progress in the control of aflatoxin contamination will depend on the introduction of technologies for specific, efficient and environmentally sound detoxification. The utilisation of biological detoxification agents, such as microorganisms and/or their enzymatic products to detoxify aflatoxins in contaminated food and feed can be a choice of such technology. To date, many of the microbial strategies have only showed reduced concentration of aflatoxins and the structure and toxicity of the detoxified products are unclear. More attention should be paid to the detoxification reactions, the structure of biotransformed products and the enzymes responsible for the detoxification. In this article, microbial strategies for aflatoxin control such as microbial binding and microbial biotransformation are reviewed.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".