Effects of processing whole oats on the analysis and fate of mycotoxins and ergosterol
Bibliographic record
Abstract
Grinding and dividing equipment were evaluated for their ability to comminute and divide ground oats in preparation for mycotoxin analysis. Four different grinders, using various settings, were evaluated for their ability to comminute oats and produce small particle sizes. Rotor beater type grinders produced the more desirable finer ground samples as compared to burr type mills. Four different division methods (manual scooping, rotary sample division, and two designs of gravity-fed dividers) were assessed for their ability to produce sub-samples with consistent particle size fraction distributions. No practical differences were observed on the particle size fraction distribution of test portions of finely ground oats produced using the four different division methods; therefore, no effects on mycotoxin analysis were anticipated. The effects of processing naturally contaminated whole oats on mycotoxin concentrations was also assessed. Laboratory scale dehulling, steaming, and kilning were examined. Dehulling showed the greatest impact and removed 60-100% of variousFusarium- andAlternaria-produced mycotoxins, as well as ergosterol, present on the naturally contaminated whole oats. Different from the other analytes studied, only 48% of the mycotoxin plant transformation product deoxynivalenol-3-glucoside was present in hulls and removed during dehulling. Steaming and kilning appeared to increase ergosterol in groats, as well as decrease deoxynivalenol and deoxynivalenol-3- glucoside. The observed inconsistent changes in concentrations of tentoxin after heat treatment of groats appeared to be due to sample heterogeneity.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".