Development of quantum dot-linked immunosorbent assay (QLISA) and ELISA for the detection of sunset yellow in foods and beverages
Bibliographic record
Abstract
Sunset yellow (SY) is widely used as food colorant. Excess and illegal use of SY could pose potential health risk. Quantum dots have been successfully used in biological research due to the high photoluminescence and high resistance to photobleaching. To analyze SY efficiently, quantum dot-linked immunosorbent assay (QLISA) and enzyme-linked immunosorbent assay (ic-ELISA) were developed on the basis of generated monoclonal antibody. A carboxyl group was introduced to SY and coupled with carrier proteins to synthesize artificial antigen. Under the optimal conditions, inhibitory concentrations (IC50) of SY were 1.9 ng/mL (ic-ELISA) and 3.4 ng/mL (QLISA); the limits of detection (LODs) were 0.2 ng/mL (ic-ELISA) and 1.0 ng/mL (QLISA), respectively. Cross-reactivities of the mAb toward eight kinds of analogues were<0.01%. The recovery rates in spiked foods and beverages were 75.6%∼120.1% (ic-ELISA) and 74.0%∼114.1% (QLISA), respectively.
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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 | 0.002 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".