Assay formats and target recognition strategies in lateral flow assays for the detection of mycotoxins
Bibliographic record
Abstract
Lateral flow assays for mycotoxins are important analytical tools because of the toxicity of mycotoxins. As mycotoxins can occur in beverages, food, and feed, there are many routes of exposure that lead to their chronic toxicity. Therefore, a monitoring method is needed that offers rapidity, reliability, specificity, and easy on-site operation. Lateral flow assays can offer quick, simple, accurate, and convenient testing. That is why, they are the perfect tool to meet this need. In recent years, many research papers have been published discussing lateral flow assays for mycotoxins. This literature review analyses the published literature of the last ten years with respect to different strategies for the detection of mycotoxins with a focus on assay formats and test strip set-up. Novel formats based on aptamers as recognition elements instead of the most common antibodies are presented. Technological differences are highlighted, and limitations of the used methods are discussed. • Review of lateral flow assays for mycotoxin detection. • Focus on different detection strategies and assay set-up. • Summary of design-strategies for ‘turn-on’ lateral flow assays and multiplexing. • Highlight on aptamer-based lateral flow assays. • Guidance on how to achieve low limits of detection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".