Advances in Aptamer-Based Biosensors for Food Safety
Bibliographic record
Abstract
The presence of unsafe levels of chemical compounds, toxins, and pathogens in food constitutes a growing public health problem that necessitates new technology for the detection of these contaminants along the food continuum from production to consumption. While traditional techniques that are highly selective and sensitive exist, there is still a need for simpler, more rapid and cost-effective approaches to food safety testing. Within this context, the field of food safety biosensors has emerged. Biosensors consist of a specific molecular recognition probe targeting an analyte of interest and a means of converting that recognition event into a measurable signal. As molecular recognition is the foundation of biosensing, there has been increased focus on the development of new molecular recognition probes for food-safety related molecular targets. Antibodies have been the gold standard in molecular recognition for several decades and have been incorporated widely into biosensors and assays relating to food (Ricci et al., 2007). Despite their applicability to food monitoring, they are not without their disadvantages, which are primarily linked to the requirement that antibody generation is an in vivo process. For example, highly toxic substances are not conducive to antibody generation. Furthermore, the batch to batch reproducibility of antibody generation can be less than satisfactory. Compounding these limitations is the fact that antibodies have short shelf-lives and can be challenging to chemically modify for incorporation into a biosensor platform. Nevertheless, the affinity and specificity of antibodies for their molecular targets make them convenient receptors for biosensing strategies. Many of the disadvantages described above could be avoided with a molecular recognition probe of synthetic origin that would still maintain the required specificity and affinity. Because of their in vitro selection and production, the relatively new technology of aptamers has emerged as a viable alternative for use in biosensor platforms (Mascini, 2009). This chapter will focus on the recent literature in aptamers for food safety related targets, as well as the biosensor platforms in which these probes have been incorporated.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".