Specific antigen‐based and emerging detection technologies of mycotoxins
Bibliographic record
Abstract
Mycotoxins are secondary fungal metabolites produced by certain types of filamentous fungi or molds, such as Aspergillus, Fusarium, Penicillium, and Alternaria spp. Mycotoxins are natural contaminants of agricultural commodities, and their prevalence may increase due to global warming. According to the Food and Agriculture Organization of the United Nations, approximately 25% of the world's food crops are annually contaminated with mycotoxins. Mycotoxin-contaminated food and feed pose a high risk to both human and animal health. For instance, they possess carcinogenic, immunosuppressive, hepatotoxic, nephrotoxic, and neurotoxic effects. Hence, various approaches have been used to assess and control mycotoxin contamination. Significant challenges still exist because of the complex heterogeneous nature of food and feed composition. The potential of antigen-based approaches, such as enzyme-linked immunosorbent assay, flow injection immunoassay, chemiluminescence immunoassay, lateral flow immunoassay, and flow-through immunoassay, would contribute to our understanding about mycotoxins' rapid identification, their isolation, and the basic principles of the detection technologies. Additionally, we address other emerging technologies of potential application in the detection of mycotoxins. The data included in this review focus on basic principles and results of the detection technologies and would be useful as benchmark information for future research. © 2019 Society of Chemical Industry.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".