Electrochemical Determination of Deoxynivalenol Using a Modified Glassy Carbon Electrode
Bibliographic record
Abstract
Mycotoxins are toxic secondary metabolites, produced by different types of molds. These substances cause economic losses and are considered as a global public health concern because the food supplies that are contaminated by mycotoxins are graded as unfit for consumption.1 Deoxynivalenol (DON), also called vomitoxin is one of the most representative mycotoxins which is mainly produced by Fusarium graminearum and Fusarium culmorum. DON commonly contaminates grains, such as corn, wheat and rice. DON has also been found to contribute to chronic disease development.2 The DON analysis for quality control purposes in grains commonly relies on expensive methods, such as GC-MS and HPLC or complex biosensors.3 These methods are used because of high sensitivity which makes it possible to detect trace amounts of DON in complex media. In this work, we propose an easy to use, rapid and cost-efficient detection method for DON based on modified glassy carbon electrodes (GCE). The electrochemical behavior of DON is studied using cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) methods. Limits of detection and quantification are determined. The performance of the sensor in spiked solutions and real samples is studied and compared with standard methods. References (1) Lee, H. J.; Ryu, D. Journal of Agricultural and Food Chemistry 2017, 65, 7034-7051. (2) Pestka, J. J.; Smolinski, A. T. Journal of Toxicology and Environmental Health, Part B 2005, 8, 39-69. (3) Tittlemier, S. A.; Brunkhorst, J.; Cramer, B.; DeRosa, M. C.; Lattanzio, V. M. T.; Malone, R.; Maragos, C.; Stranska, M.; Sumarah, M. W. World Mycotoxin Journal 2021, 14, 3-26.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".