Design Principles of Nanosensors for Multiplex Detection of Contaminants in Food
Bibliographic record
Abstract
The rapid and cost-effective detection of food contaminants such as toxins and pathogens is a major challenge and a key concern for food safety. To this end, innovative, fast, cost-effective, and easy-to-use sensors must be developed at the point where food is produced, distributed, and consumed. Therefore, timely detection and response to food contaminants can improve human health and reduce economic burden. However, affordable sensor technologies with specificity, sensitivity, and speed are required, which can be used by non-specialized personnel and enable high throughput analysis. In this respect, advances in the development of nanoparticle-based sensors, i.e., nanosensors, have shown the potential to provide the much-anticipated versatile sensors. In addition, multiplex detection, i.e., the ability to detect multiple targets simultaneously, is another strategy facilitated by nanoparticle-based sensors and will enable further improvements in sensor performance that are important for developing effective monitoring. This review summarizes the nanosensors for multiplex sensing of food samples with respect to hazardous contaminates reported over the past few years. In addition, special attention is paid to providing the reader with promising design principles and the current performance of the sensitivity and selectivity of such sensors for practical requirements, thereby inspiring new ideas for developing further advanced systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".