Rapid quantification of aflatoxin in food at the point of need: A monitoring tool for food systems dashboards
Bibliographic record
Abstract
Aflatoxins (AFs) are naturally occurring mycotoxins known to cause a considerable threat to food safety and affect animal and health. Rapid and reliable analytical methods are crucial for preventing AF contamination in the global food supply chain. Many conventional AF detection methods involve complex sample preparation steps, lengthy analysis times, and multiple handling stages which lead to delays in obtaining results, in addition to being unaffordable in many settings with resource constraints. Herein, we demonstrate the proof of concept of a competitive immunochromatographic (IC) strip test for quantification of total AF using commercially available antibodies and a low-cost portable CubeTM analyzer. We conducted preliminary testing of our point-of-need (PON) AF detection method with corn samples and results indicated a good agreement when compared with gold standard HPLC method. Furthermore, a detection range of 5–50 ppb with detection time of 5 min, makes this technology suitable for rapid testing and meets the regulatory requirements for AF detection in food samples. We also demonstrate the real-time data sharing capabilities of the reader to a proof-of-concept centralized and cloud-based AF databank, that we developed to provide timely monitoring for different parts of food systems. It is critical for the test data to be easily accessible within a food systems dashboard to enable early warning, data-driven decision-making, rapid interventions, and improve overall coordination between various stakeholders within the food system.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".