Advancing In Situ Food Monitoring through a Smart Lab‐in‐a‐Package System Demonstrated by the Detection of <i>Salmonella</i> in Whole Chicken
Bibliographic record
Abstract
Abstract With food production shifting away from traditional farm‐to‐table approaches to efficient multistep supply chains, the incidence of food contamination has increased. Consequently, pathogen testing via inefficient culture‐based methods has increased, despite its lack of real‐time capabilities and need for centralized facilities. While in situ pathogen detection would address these limitations and enable individual product monitoring, accurate detection within unprocessed, packaged food products without user manipulation has proven elusive. Herein, “Lab‐in‐a‐Package” is presented, a platform capable of sampling, concentrating, and detecting target pathogens within closed food packaging, without intervention. This system consists of a newly designed packaging tray and reagent‐infused membrane that can be paired universally with diverse pathogen sensors. The inclined food packaging tray maximizes fluid localization onto the sensing interface, while the membrane acts as a reagent‐immobilizing matrix and an antifouling barrier for the sensor. The platform is substantiated using a newly discovered Salmonella‐responsive nucleic acid probe, which enables hands‐free detection of 103 colony forming units (CFU) g−1 target pathogen in a packaged whole chicken. The platform remains effective when contamination is introduced with toolsand surfaces, ensuring widespread efficacy. Its real‐world use for in situ detection is simulated using a handheld fluorescence scanner with smartphone connectivity.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".