Mechanisms of deoxynivalenol (DON) degradation during different treatments: a review
Bibliographic record
Abstract
Deoxynivalenol (DON) is one of the main trichothecenes, that causes health-related issues in humans and animals and imposes considerable financial loss to the food industry each year. Numerous treatments have been reported in the literature on the degradation of DON in food products. These treatments include thermal, chemical, biological/enzymatic, irradiation, light, ultrasound, ozone, and atmospheric cold plasma treatments. Each of these methods has different degradation efficacy and degrades DON by a distinct mechanism, which leads to various degradation byproducts with different toxicity. This manuscript focuses to review the degradation of DON by the aforementioned treatments, the chemical structure and toxicity of the byproducts, and the degradation pathway of DON. Based on the type of treatment, DON can be degraded to norDONs A-F, DON lactones, and ozonolysis products or transformed into de-epoxy deoxynivalenol, DON-3-glucoside, 3-acetyl-DON, 7-acetyl-DON, 15-acetyl-DON, 3-keto-DON, or 3-epi-DON. DON is a major problem for the grain industry and the studies focusing on DON degradation mechanisms could be helpful to select the best method and overcome the DON contamination in grains.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".