By-products of grain cleaning: an opportunity for rapid sampling and screening of wheat for mycotoxins
Bibliographic record
Abstract
By-products of cereal grain cleaning were analysed for a number of mycotoxins. Deoxynivalenol (DON) was the most frequently detected in by-products from commercial-scale cleaning procedures (maximum 2.94 mg/kg), followed by zearalenone (ZEA; maximum 0.045 mg/kg) and ochratoxin A (OTA; maximum 0.019 mg/kg). These three mycotoxins were also the most frequently detected in four different fractions collected from wheat run through a dockage tester, a piece of equipment used in the Canadian inspection process to separate material other than grain from wheat. Concentrations of mycotoxins were highest in the ‘light dockage’ fraction that contained dust and roughage such as glumes, fragments of stem, or rachis. Mycotoxin concentrations in this fraction reached up to 32 mg/kg (DON), 0.532 mg/kg (ZEA), and 0.249 mg/kg (OTA). Concentrations of DON in light dockage were significantly correlated with concentrations in whole grain that was un-cleaned or had undergone basic cleaning, indicating that the light dockage fraction could be used as a readily available matrix for the rapid screening of DON in wheat. This would eliminate the time required for additional sampling and preparation of whole grain, and move towards a truly rapid method for the screening of DON in wheat.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".