Determination of mycotoxins in nuts, cereals, legumes, and coffee beans and effectiveness of a selenium‐based decontamination treatment
Bibliographic record
Abstract
Abstract Liquid chromatography‐tandem mass spectrometry (LC‐MS/MS) was used for the rapid quantification of multiple mycotoxins, specifically aflatoxins B1, B2, G1, and G2 (AFB1, AFB2, AFG1, and AFG2), ochratoxin A (OTA), deoxynivalenol (DON), and zearalenone (ZEN), in walnuts, pistachios, peanuts, coffee beans, rice, and chickpeas from various countries. Total counts of fungi, Aspergillus flavus, and Aspergillus parasiticus were also assessed, along with the effectiveness of a decontamination treatment with inorganic selenium to reduce mycotoxin levels. Of the 78 samples tested, 69% were contaminated with mycotoxins. ZEN, the predominant mycotoxin contaminant, was detected in all the contaminated samples in concentrations often exceeding the maximum level, followed by AFG1 (28% of the contaminated samples), DON (22%), AFG2 (11%), and AFB1 (5.5%). The occurrence of aflatoxins was associated with high proportions of A. flavus and A. parasiticus. Complete removal of AFB1 from walnuts and DON from roasted coffee beans was achieved by treatment with aqueous selenium, while the levels of ZEN and AFG1 were respectively lowered by 65% to 89% depending on the commodity and by about 56% in roasted coffee beans. While this novel treatment is a promising approach for mycotoxin decontamination, it is not intended to replace safe practices upstream.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".