4.1 Article

Real-Time and Rapid Food Quality Monitoring Using Smart Sensory Films with Image Analysis and Machine Learning

Journal

ACS FOOD SCIENCE & TECHNOLOGY
Volume 2, Issue 7, Pages 1123-1134

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acsfoodscitech.2c00124

Keywords

bio-based sensory film; KNN learning; food spoilage detection; image analysis

Funding

  1. U.S. Department of Agriculture-National Institute of Food and Agriculture (USDA-NIFA) [2016-11231]
  2. USDA-NIFA, National Needs Fellowship Program (NNF) [2015-38420-23701]
  3. GEM National Consortium Fellowship Program

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This study developed a real-time, simple, and user-friendly food quality detection prototype by combining a glycerol-based sensory film with visual color analysis and the k-nearest neighbors algorithm. It provides technology for accurate monitoring of food freshness and spoilage degree, suitable for large-scale food storage applications.
Detecting and reporting the quality of packaged food to the consumer in real-time can reduce the consumption of poor-quality food products. Current food quality detection and reporting technologies of perishable foods are usually expensive, complicated, and take a significantly long time to convey results. Herein, a real-time, simple, and user-friendly food freshness detection prototype was developed by combining a glycerol-based sensory film with unique visual color analysis and the k-nearest neighbors algorithm (KNN). We established the quantitative relationship between the pH, organic acid level, digital color variance, and food storage time. By measuring the color variations of sensor films as a function of food storage time, we demonstrate a technology to record the quantitative RGB values of sensory films to represent real-time and precise pH changes of the food sample and trace the real-time food spoilage degree (e.g., pork loin spoilage). Next, a quick-response (QR) reader with a center sensor film was designed to eliminate the environmental effects on the color variation in real time Furthermore, the KNN was implemented to classify food quality by training data from different sources. This study provides a technology well suited for large-scale food storage applications by combining a smart sensor film with a QR code design followed by image analysis and KNN. This real-time and rapid food quality monitoring technology will ultimately lead to a reduction in food waste and loss (FLW).

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