Improvement of the sensitivity of lateral flow systems for detecting mycotoxins: Up‐to‐date strategies and future perspectives
Bibliographic record
Abstract
Mycotoxins are dangerous human and animal health-threatening secondary fungal metabolites that can be found in various food and agricultural products. Several countries have established regulations to restrict their presence in food and agricultural products destined for human and animal consumption. Consequently, the need to develop highly sensitive and smart detection systems was recognized worldwide. Lateral flow assay possesses the advantages of easy operation, rapidity, stability, accuracy, and specificity, and it plays an important role in the detection of mycotoxins. Nevertheless, strategies to comprehensively improve the sensitivity of lateral flow assay to mycotoxins in food have rarely been highlighted and discussed. In this article, a comprehensive overview was presented on the application of lateral flow assay in mycotoxin detection in food samples by highlighting the principle of lateral flow assay, presenting a detailed discussion on various analytical performance-improvement strategies, such as the development of high-affinity recognition reagents, immunogen immobilization methods, and signal amplification. Additionally, a detailed discussion on the various signal analyzers and interpretation approaches was provided. Finally, current hurdles and future perspectives on the application of lateral flow assay in the detection of mycotoxins were discussed.
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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 | 0.005 | 0.003 |
| 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 | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".