Nanomaterial-based fluorescent biosensor for veterinary drug detection in foods
Bibliographic record
Abstract
Veterinary drugs have been widely used in the food industry. Their residues in food products need to be tightly regulated to ensure food safety. In particular, some veterinary drugs are still used illegally, although they have already been banned. Nanomaterials are playing an increasingly important role in analytical chemistry due to their unique properties. Compared to traditional organic dyes and colloidal gold nanoparticle labels, fluorescent nanomaterials appear particularly attractive for the detection of veterinary drug residues. This review summarizes recent advancements of fluorescent biosensors using nanomaterials for the detection of veterinary drug residues in foods. The useful properties of each type of fluorescent nanomaterial are first discussed such as large Stokes shifts, long emission lifetime, and high quantum yields, which are useful for detection in food-related sample matrix. The following target recognition molecules are then reviewed individually including antibodies, aptamers, molecularly imprinted polymers, and metal ion coordination based ligands. Representative bioconjugation and assay methods are discussed for each recognition mechanism. Finally, a few future research directions are outlined in the last section of this review.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".