Recent Advances in Electrochemical Sensors for Mycotoxin Detection in Food
Bibliographic record
Abstract
Abstract Mycotoxins pose a grave global threat to human life and health by contaminating food and feed and cause enormous losses in healthcare and trading. Trace mycotoxin concentrations and diverse matrices in food make identification and measurement challenges, necessitating highly specific and sensitive detection methods. Electrochemical (EC) sensors are characterized by simple operation, outstanding sensitivity, low cost, and facile miniaturization and have become a promising strategy for addressing specificity and sensitivity in detection. Recent studies on EC sensors for mycotoxin detection for food safety are reviewed here. First, we summarize the fabrication of EC sensors and techniques with enhanced specificity and sensitivity. Then, we review state‐of‐the‐art EC sensors for detecting major mycotoxins. Challenges and opportunities for this technology are further discussed. Finally, in‐depth information is provided on using EC sensors to detect mycotoxins for food safety, as well as the development of EC sensors for academic study and practical application.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".