Innovative Infrared Spectroscopic Technologies for the Prediction of Deoxynivalenol in Wheat
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Mycotoxin contamination in cereals is a global food safety concern. One of the most common mycotoxins in grains is deoxynivalenol (DON), a secondary metabolite produced by the fungi Fusarium graminearum and Fusarium culmorum . Exposure to DON can lead to adverse health effects in both humans and animals including vomiting, dizziness, and fever. Hence, the development of analytical technologies capable of predicting mycotoxin contamination levels in grains is crucial. In this study, we emphasize innovative infrared (IR) spectroscopic technologies for the prediction of DON in wheat along the food supply chain. The performance of an IR laser spectroscopic platform for on-site or laboratory confirmative analysis was evaluated. Furthermore, the performance of a handheld IR spectrometer for preliminary screening during transportation, storage, or harvesting was assessed. The accuracy of cross validation (Acc CV ) obtained with the laser spectrometer reached 92%, while the handheld IR spectrometer achieved 84.6%. Hence, both technologies prove significant potential for rapid mycotoxin detection.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
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".