Quantum Dots in the Analysis of Food Safety and Quality
Bibliographic record
Abstract
The detection of chemical residues, toxins, pathogens and allergens contaminating food and water is of utmost importance to society. Although numerous strategies have been developed to detect, isolate and identify potential threats in food, there remains great demand for assays that enhance the speed, sensitivity and selectivity of detection in formats that are simple, portable and low cost. Quantum dots are brightly fluorescent semiconductor nanocrystals with many physical and optical properties that can help address the challenges associated with developing improved assays for food safety and quality. This chapter summarizes research toward the utilization of quantum dots in assays for the detection of analytes such as pathogens, pesticides, antibiotics and genetically modified organisms (GMOs). A short primer on the properties and bioconjugation of quantum dots is also included. Numerous studies have demonstrated the potential for quantum dots to enhance analytical figures of merit in food safety and quality assays; however, strategic research is needed to develop quantum dot-enabled assays that will have the greatest opportunity to impact food safety practices in industry and society.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".