Sentinel Wraps: Real-Time Monitoring of Food Contamination by Printing DNAzyme Probes on Food Packaging
Bibliographic record
Abstract
Here, we report the development of a transparent, durable, and flexible sensing surface that generates a fluorescence signal in the presence of a specific target bacterium. This material can be used in packaging, and it is capable of monitoring microbial contamination in various types of food products in real time without having to remove the sample or the sensor from the package. The sensor was fabricated by covalently attaching picoliter-sized microarrays of an E. coli -specific RNA-cleaving fluorogenic DNAzyme probe (RFD-EC1) to a thin, flexible, and transparent cyclo-olefin polymer (COP) film. Our experimental results demonstrate that the developed (RFD-EC1)-COP surface is specific, stable for at least 14 days under various pH conditions (pH 3–9), and can detect E. coli in meat and apple juice at concentrations as low as 10 3 CFU/mL. Furthermore, we demonstrate that our sensor is capable of detecting bacteria while still attached to the food package, which eliminates the need to manipulate the sample. The developed biosensors are stable for at least the shelf life of perishable packaged food products and provide a packaging solution for real-time monitoring of pathogens. These sensors hold the potential to make a significant contribution to the ongoing efforts to mitigate the negative public-health-related impacts of food-borne illnesses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".