Aptamer-based biosensor for food allergen determination using graphene oxide/gold nanocomposite on a paper-assisted analytical device
Bibliographic record
Abstract
Abstract The detection of allergens in food are currently conducted by techniques that are time-consuming and complicated which can deter consistent sampling for allergens, which could potentially cause an anaphylactic shock in the consumer by cross-contamination. The need for a technique that is rapid, on-site, cost-effective, disposable, highly sensitive and accurate to identify these molecules urges the development of a point-of-care device. The aim of this work is to develop a microfluidic paper-assisted analytical device (PAD) using hydrophobic channels, set by a wax printer on filter paper, and functionalized gold nanoparticles (AuNP) to help identify the allergens arachin (Ara h 1) for peanuts, β-lactoglobulin (β LG) for milk, and tropomyosin (Pen a 1) for shrimp and other shellfish presence by a colorimetric test. Synthesized AuNP were conjugated with biotinylated aptamers, using the biotin-streptavidin interaction, to make the specific detection of target allergens. Functionalized AuNP are incubated with the sample and are absorbed by graphene oxide (GO), creating GO-AuNP complexes, if the aptamers have not become structured due to conjugation with allergenic proteins. The PAD device is used to filter the resultant mixture which provides superior sensitivity to detect the allergens present down to the nanogram range (allergens were measured from 25 nM - 1000 nM with a LOD of 7.8 nM, 12.4 nM and 6.2 nM for peanut, milk and shrimp allergens respectively), in contrast to the microgram range of commonly used enzymatic immunoassays. The simple color indicator, varying from clear to pink in the presences of allergens allows the readout to be utilized without the need for highly specific equipment or training. Alternatively, the results can be quantified by taking a picture and measuring the color. This presented PAD can provide results in real time and has the potential to become a rapid, low-cost, and accurate portable point-of-care device to avoid cross-reactivity of food-borne allergens.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".