A High-Throughput Fast Chromatography-Tandem Mass Spectrometry-Based Method for Deoxynivalenol Quantification in Wheat Grain
Bibliographic record
Abstract
Fusarium head blight (FHB), caused by Fusarium spp., is a destructive disease of cereal grains. Apart from grain yield loss, a major quality concern is contamination with Fusarium-produced mycotoxins, specifically deoxynivalenol (DON). Mycotoxins accumulate in the grain, making it unfit for consumption by humans and animals. Breeding cultivars with high disease resistance and low mycotoxin contamination is a priority for wheat breeders. However, DON measurement in breeding programs is expensive and time consuming due to the lack of efficient quantification methods. In this study, we established a simple fast chromatography-tandem mass spectrometry method, which employed a one-step acetonitrile extraction protocol with a short guard column to reduce complexity, cost, and analysis time. To ensure robustness and reproducibility, the method was validated according to the U.S. Food and Drug Administration Guidance for Bioanalytical Method Validation. Furthermore, the method was applied for determination of DON in 102 wheat grain samples. Obtained results highly correlated with the conventional immunological method for all tested samples. With its ease of use, rapid sample analysis, and high sensitivity and accuracy, the method could be integrated into current FHB breeding programs to increase breeding efficiency and accelerate screening progress to identify germplasm with increased resistance to DON accumulation. [Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".