Machine Learning enabled food contamination detection using RFID and Internet of Things system

Sharif, A., Abbasi, Q. H. , Arshad, K., Ansari, S. , Ali, M. Z., Kaur, J. , Abbas, H. T. and Imran, M. A. (2021) Machine Learning enabled food contamination detection using RFID and Internet of Things system. Journal of Sensor and Actuator Networks, 10(4), 63. (doi: 10.3390/jsan10040063)

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.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Ansari, Dr Shuja and Imran, Professor Muhammad and Kaur, Jaspreet and Abbasi, Professor Qammer
• Authors: Sharif, A., Abbasi, Q. H., Arshad, K., Ansari, S., Ali, M. Z., Kaur, J., Abbas, H. T., and Imran, M. A.
• College/School: College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering; College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: Journal of Sensor and Actuator Networks
• Publisher: MDPI
• ISSN: 2224-2708
• ISSN (Online): 2224-2708
• Copyright Holders: Copyright © 2021 The Authors
• First Published: First published in Journal of Sensor and Actuator Networks 10(4):63
• Publisher Policy: Reproduced under a Creative Commons licence