Rapid Detection of Food Allergens by Microfluidics ELISA-Based Optical Sensor
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The risks associated with the presence of hidden allergens in food have increased the need for rapid, sensitive, and reliable methods for tracing food allergens in commodities. Conventional enzyme immunosorbent assay (ELISA) has usually been performed in a centralized lab, requiring considerable time and sample/reagent consumption and expensive detection instruments. In this study, a microfluidic ELISA platform combined with a custom-designed optical sensor was developed for the quantitative analysis of the proteins wheat gluten and Ara h 1. The developed microfluidic ELISA biosensor reduced the total assay time from hours (up to 3.5 h) to 15-20 min and decreased sample/reagent consumption to 5-10 μL, compared to a few hundred microliters in commercial ELISA kits, with superior sensitivity. The quantitative capability of the presented biosensor is a distinctive advantage over the commercially available rapid methods such as lateral flow devices (LFD) and dipstick tests. The developed microfluidic biosensor demonstrates the potential for sensitive and less-expensive on-site determination for rapidly detecting food allergens in a complex sample system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it