Rapid and sensitive double‐label based immunochromatographic assay for zearalenone detection in cereals
Bibliographic record
Abstract
Abstract A double‐label immunochromatographic based assay (DL‐ICA) was developed to monitor zearalenone (ZEN) levels in cereals, based on Eu 3+ nanoparticles (EuNP). The DL‐ICA exhibited excellent sensitivity, reliability and selectivity in real samples. It showed low limits of detection (0.21–0.25 μg/kg) and broad analytical ranges (up to 120 μg/kg). The total analytical time, including sample preparation and DL‐ICA execution, was reduced by 15 min compared with HPLC. The recovery rates ranged from 95.0–118.4%, with relative standard deviations (RSD) <11.6%. Inter‐ and intra‐day validations were assessed, recovery rates of 89.3–106.9% and RSD of 2.3–9.7% were obtained, suggesting considerable stability and reliability for the assay. An excellent correlation was observed between DL‐ICA and a reference HPLC method ( R 2 = 0.9899). Compared to current immunoassays, the current DL‐ICA is inexpensive, highly sensitive, and rapid. Therefore, DL‐ICA constitutes a novel tool for monitoring mycotoxins in food and feed to ensure safety.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".