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Hollow Microneedle-based Sensor for Multiplexed Transdermal Electrochemical Sensing
Published on: June 1, 2012
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Food-Activated Microneedle Sensor for Real-Time, Colorimetric Spoilage Monitoring of Pre-Packaged Food
Shadman Khan1,2, Akansha Prasad2, Mahum Javed2
1School of Environmental Sciences, University of Guelph, Guelph, ON, N1G 2W1, Canada.
Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|November 12, 2025
Summary
A new, inexpensive colorimetric spoilage sensor uses gelatin microneedles with anthocyanins to detect food spoilage via pH changes. This technology empowers consumers with real-time food quality monitoring.
Area of Science:
- Food Science
- Materials Science
- Analytical Chemistry
Background:
- Growing global food insecurity necessitates innovative solutions to minimize food waste.
- Current methods for assessing food spoilage are often time-consuming, expensive, or require laboratory equipment.
- There is a critical need for accessible, real-time food spoilage detection technologies.
Purpose of the Study:
- To develop an inexpensive, colorimetric sensor for real-time assessment of food product spoilage.
- To create a sensor utilizing dehydrated gelatin microneedles that transition to a hydrogel sensing state in food environments.
- To embed food-derived anthocyanins for pH-based spoilage monitoring within the microneedle sensor.
Main Methods:
- Fabrication of dehydrated gelatin microneedles incorporating food-derived anthocyanins.
- Application of sensors to sealed and unsealed fish products for non-destructive and rapid testing.
- Colorimetric analysis of sensor response correlated with quantitative spoilage markers.
- Utilizing machine learning for image-based categorization of sensor color shifts (fresh vs. spoiled).
Main Results:
- The microneedle sensors demonstrated rapid transition to a hydrogel state upon contact with food.
- A distinct color shift in the sensor correlated strongly with quantitative food spoilage markers.
- The sensor successfully penetrated packaging of sealed fish products for in-situ monitoring.
- Smartphone image analysis with machine learning accurately categorized fresh versus spoiled fish products.
Conclusions:
- The developed microneedle-based colorimetric sensor offers an inexpensive and effective method for real-time food spoilage detection.
- This technology has the potential to significantly reduce food waste by empowering consumers and producers with independent monitoring capabilities.
- The integration of machine learning enhances the sensor's reliability by removing readout ambiguity.
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