Mechanisms and application of mycotoxin decontamination techniques in stored grains
Bibliographic record
Abstract
Ensuring the safe storage of food grains is paramount for global food security. However, mycotoxin contamination poses a significant threat by compromising grain quality and consumer health. Various decontamination techniques are employed to inactivate toxins, each with distinct mechanisms of toxin inactivation. This review examines the pivotal mechanisms in reducing mycotoxin levels in stored grains, elucidating the principles and pathways underlying novel decontamination techniques such as cold plasma, ozone, photocatalysis, nanoparticle adsorbents, and microbial enzymes, and assesses their practical application and industrial feasibility. Our thorough investigation reveals that the effectiveness of decontamination techniques relies on three fundamental mechanisms: adsorption, treatment with reactive chemical species, and biotransformation. Several novel technologies are highly effective in laboratory tests, but face challenges at the industrial scale. Current research indicates that novel decontamination techniques, including pulsed light, photocatalysis, and microbial enzymes, hold much promise in significantly reducing fungal growth and mycotoxin contamination in grains. However, it is also evident that techniques with high efficacy in reducing fungal infestations are not necessarily effective in eradicating mycotoxin contamination. A combinatory approach to these techniques is the way forward, and future research should focus on hybrid treatments to enhance the effectiveness of these technologies on an industrial scale. This review aims to bolster food safety and mitigate economic losses linked to mycotoxin contamination in grains by offering a theoretical basis for developing and implementing effective decontamination strategies. • Novel techniques show promise for reducing mycotoxins in stored grains. • Cold plasma, ozone, and photocatalysis work but face industrial challenges. • Mechanisms reviewed with a focus on industrial applicability and limitations. • Combining methods may improve mycotoxin removal on an industrial scale.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".