Mycotoxin Testing Paradigm: Challenges and Opportunities for the Future
Bibliographic record
Abstract
Mycotoxins are one of the great global challenges to agri-food and feed safety. Industry requires fast, reliable, and economical testing methods for the most important regulated mycotoxins to manage this problem. Climate change and changes in agricultural practice are complicating this situation, triggering the movement of some mycotoxins into new regions, which are unprepared for their management. Modern LC-tandem MS (LC-MS/MS) instruments have addressed this analytical challenge, but such instruments are expensive and require highly qualified personnel and dedicated facilities. As a result of these limitations, traditional LC-MS/MS is not amenable for use on farms or at small to midsized processing facilities, such as a grain elevator. To address the need for on-site rapid testing, the mycotoxin community has focused on antibody-based and spectrophotometric approaches. The development of innovative technologies such as miniaturized MS would allow for the acquisition of more information on mixtures of toxins present in a sample at costs comparable to those of the existing rapid methods such as ELISA. The capital costs are higher, but it would reduce per-sample testing costs and time requirements and provide better value for money while maintaining the accuracy and selectivity achieved in a laboratory setting. In this article, we review the available techniques and contrast them in the context of three main criteria: method performance, speed of analysis, and cost. We define the integration of these three parameters as the "mycotoxin testing paradigm."
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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 | 0.038 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".