Food‐Activated Microneedle Sensor for Real‐Time, Colorimetric Spoilage Monitoring of Pre‐Packaged Food
Bibliographic record
Abstract
At a time of growing food insecurity, developing technologies to reduce food waste is critical. An inexpensive, colorimetric spoilage sensor for real-time food product assessment is reported. The sensor is composed of dehydrated gelatin microneedles that exhibit high mechanical integrity in their base state. However, once exposed to fluid-rich food environments, they rapidly transition to a hydrogel sensing state. Food-derived anthocyanins embedded within these microneedles enable pH-based spoilage monitoring. When applied to sealed fish products, these microneedles nondestructively penetrate through the packaging and are rehydrated by the underlying fish matrix. As the product ages, a defined color shift occurs, demonstrating strong correlation with quantitative spoilage markers. When applied to unsealed fish products for rapid testing, the large microneedle sensing interface enables accelerated colorimetric sensing. Finally, successful fresh vs spoiled categorization of smartphone-acquired images of the sensor using machine learning removes readout ambiguity, empowering consumers with independent real-time product monitoring.
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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 | 0.000 |
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".