Aptamer-based Sensing Techniques for Food Safety and Quality
Bibliographic record
Abstract
Food safety is a growing public health concern worldwide. The need to detect unsafe levels of food contaminants such as chemical compounds, toxins and pathogens prompts new technology and advances in biosensing for food safety. Although current detection methods are able to detect such contaminants with a high level of selectivity and sensitivity, these methods continue to lack practical application. A reliable, easy-to-use, inexpensive detection method that can be used quickly and on-site is a necessity, especially for contaminants that primarily affect food commodities in developing countries. Aptamers are single-stranded oligonucleotides capable of binding a specific target molecule with a high degree of affinity and selectivity. These molecular recognition elements can be selected to bind selectively to a specific target molecule, ranging from small molecules to whole cells. This allows aptamers to be used as the recognition components for food-safety related biosensors. This chapter will review recent literature in aptamers for food-safety related target molecules, and will focus on the incorporation of these aptamers in sensitive and practical biosensors for a variety of food products.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.014 |
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".