Bibliographic record
Abstract
ABSTRACT Food analysis is requiring rapid, accurate, sensitive and cost-effective methods to monitor and guarantee the safety and quality to fulfill the strict food legislation and consumer demands. In our study, a nano-materials enhanced multipurpose paper based microfluidic aptasensor was demonstrated as a sensing tool for accurate detection of food allergens and food toxins. Graphene oxide (GO) and specific aptamer - functionalized quantum dots (QDs) were employed as probes, the fluorescence quenching and recovering of the QDs caused by the interaction among GO, aptamer-functionalized QDs and the target protein were investigated to quantitatively analyze the target concentration. The homogenous assay was performed on the paper based microfluidic chip, which significantly decreased the sample and reagent consumptions and reduced the assay time. Egg white lysozyme, ß-conglutin lupine and food toxins, okadaic acid and brevetoxin standard solutions and spiked food samples were successfully assayed by the presented aptasensor. Dual-target assay was completed within 5 min, and superior sensitivities were achieved when testing the samples with commercial ELISA kits side by side.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".