Developments in analytical techniques for mycotoxin determination: an update for 2023-24
Bibliographic record
Abstract
Abstract This review summarises developments published in the period from mid-2023 to mid-2024 on the quantitative and qualitative determination of mycotoxins in a various matrices. Important developments in all aspects of mycotoxin analysis, from sampling and quality assurance/quality control of analytical results, to the various detection and quantitation technologies ranging from biosensors to comprehensive instrumental methods are presented and discussed. This non-exhaustive summary and associated discussion covers such technology as chromatography with targeted or non-targeted high resolution mass spectrometry, detection other than mass spectrometry such as fluorescence or diode array detection, biosensors, assays using alternatives to antibodies, as well as methods using predictive modelling and incorporating machine learning. This collaborative critical review intends to guide readers to relevant research by briefly presenting the most important developments in mycotoxin determination published in the past year. This review also relays limitations of the presented methodologies, in order to provide a thorough assessment of the analytical developments.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 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) | 0.008 | 0.008 |
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".