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- Title
Machine Learning Enabled Food Contamination Detection Using RFID and Internet of Things System.
- Authors
Sharif, Abubakar; Abbasi, Qammer H.; Arshad, Kamran; Ansari, Shuja; Ali, Muhammad Zulfiqar; Kaur, Jaspreet; Abbas, Hasan T.; Imran, Muhammad Ali
- Abstract
This paper presents an approach based on radio frequency identification (RFID) and machine learning for contamination sensing of food items and drinks such as soft drinks, alcohol, baby formula milk, etc. We employ sticker-type inkjet printed ultra-high-frequency (UHF) RFID tags for contamination sensing experimentation. The RFID tag antenna was mounted on pure as well as contaminated food products with known contaminant quantity. The received signal strength indicator (RSSI), as well as the phase of the backscattered signal from the RFID tag mounted on the food item, are measured using the Tagformance Pro setup. We used a machine-learning algorithm XGBoost for further training of the model and improving the accuracy of sensing, which is about 90%. Therefore, this research study paves a way for ubiquitous contamination/content sensing using RFID and machine learning technologies that can enlighten their users about the health concerns and safety of their food.
- Subjects
FOOD contamination; MACHINE learning; INTERNET of things; RADIO frequency identification systems; INFANT formulas; FOOD safety
- Publication
Journal of Sensor & Actuator Networks, 2021, Vol 10, Issue 4, p63
- ISSN
2224-2708
- Publication type
Article
- DOI
10.3390/jsan10040063