Evaluation of treatment methods for spiking deoxynivalenol (DON) in single corn kernels
Bibliographic record
Abstract
Current research on deoxynivalenol (DON), a trichothecene secondary metabolite produced by the Fusarium species in corn grains, relies on the time-consuming field inoculation of Fusarium species with unpredictable and low recovery rates of DON in grains. The objective of this study was to evaluate three methods: soaking whole grains, soaking cracked grains, and injection in three solvents (distilled water, methanol, and acetonitrile) at three toxin concentration levels (1, 5, and 10 µg/g) for facilitating DON absorption in corn grains. The effectiveness of each treatment method and the performance of each solvent in aiding DON absorption were analysed and compared with the recovery rates of DON in the treated corn grains. The treatment methods, solvent, and DON concentration in solvent had significant effect on the recovery rate of DON in treated kernels. Injecting whole grains showed the highest recovery rates of DON (60%–108%) followed by soaking cracked grains (10%–87%) and whole grain (10%–72%) treatment methods. Distilled water showed the highest recovery rates in both soaking (53%–87%) and injection (74%–105%) treatment methods followed by methanol (18%–68% for soaking; 66%–103% for injection) and acetonitrile (10%–36% for soaking; 61%–108% for injection). Water dispersed the arrangement of starch granules but caused no changes in their surface morphology. Methanol and acetonitrile showed disruptive effects on the surface morphology of starch granules.
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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) | 0.000 | 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".