Food-activated Microneedle Sensor for Real-time, Colorimetric Spoilage Monitoring of Pre-packaged Food
Bibliographic record
Abstract
Abstract At a time of growing food insecurity, developing technologies to reduce food waste is critical. We report an inexpensive, colorimetric spoilage sensor for real-time food product assessment. 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 non-destructively penetrate through packaging and are rehydrated by the underlying fish matrix. As the product ages, a defined colour 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 versus 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".