Sensing and Characterization of Agricultural Mycotoxins Using Electrochemistry
Bibliographic record
Abstract
Mycotoxins are highly toxic and can be found as contaminants in products derived from cereals and grains.1 According to the Canadian Food Inspection Agency (CFIA), at least 25% of the grain produced each year worldwide present mycotoxin contamination.2 Among the mycotoxins of major concern in Canada is ochratoxin A (OTA),2 which is produced mainly by Aspergillus fungi species.3 Because of its severely negative health effects, the detection of this compound in grain and food samples is highly relevant and regulated by the legislation.2 OTA has been detected in food products and commodities through costly and time-consuming methods, such as chromatography.4 Existing electrochemical sensors are still based on systems that are too complex and expensive for industrial applications.5,6 Herein, we propose a rapid, reliable and cost-efficient sensing strategy for OTA detection, which is based on glassy carbon electrodes (GCE) modified with PtNPs.7 OTA is characterized for oxidation and reduction potentials, diffusion behaviour, and limits of detection and quantification. To this end, we aim to develop a practical and highly sensitive electroanalytical methodology that will enable us to detect OTA in grain samples in the future. References B. Scafuri et al., Scientific Reports, 7, 1–11 (2017). L. L. Charmley and H. L. Trenholm, RG-8 Regulatory Guidance: Contaminants in Feed (2017) https://inspection.canada.ca/animal-health/livestock-feeds/regulatory-guidance/rg-8/eng/1347383943203/1347384015909?chap=1. T. R. Bui-Klimke and F. Wu, Critical Reviews in Food Science and Nutrition, 55, 1860–1869 (2015). R. Chauhan, J. Singh, T. Sachdev, T. Basu, and B. D. Malhotra, Biosensors and Bioelectronics, 81, 532–545 (2016). A. Suea-Ngam, P. D. Howes, C. E. Stanley, and A. J. Demello, ACS Sensors, 4, 1560–1568 (2019). K. Kunene et al., Sensors and Actuators, B: Chemical, 305 (2020). E. Mazzotta et al., ACS Applied Nano Materials, 4, 7650–7662 (2021).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".