Paper-Based Microfluidic Device (DON-Chip) for Rapid and Low-Cost Deoxynivalenol Quantification in Food, Feed, and Feed Ingredients
Bibliographic record
Abstract
Mycotoxin contamination causes over $5 billion of economic loss per year in the North American food and feed industry. A rapid, low-cost, portable, and reliable method for on-site detection of deoxynivalenol (DON), a representative mycotoxin predominantly occurring in grains, would be helpful to control mycotoxin contamination. In this study, a paper-based microfluidic chip capable of measuring DON (DON-Chip) in food, feed, and feed ingredients was developed. The DON-Chip incorporated a colorimetric competitive immunoassay into a paper microfluidic device and used gold nanoparticles as a signal indicator. Furthermore, a novel ratiometric analysis method was proposed to improve detection resolvability. Detection of DON in the aqueous extracts from solid food, feed, or feed ingredients was successfully validated with a detection range of 0.01–20 ppm (using dilution factors from 10 to 10 4 ). Compared with conventional methods, the DON-Chip method could greatly reduce the cost and time of mycotoxin detection in the food and feed industry.
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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) | 0.001 | 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".