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A Machine-Learning Based Microwave Sensing Approach to Food Contaminant Detection
- Source :
- ISCAS, Scopus-Elsevier
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- To detect contaminants accidentally included in packaged foods, food industries use an array of systems ranging from metal detectors to X-ray imagers. Low density plastic or glass contaminants, however, are not easily detected with standard methods. If the dielectric contrast between the packaged food and these contaminants in the microwave spectrum is sensible, Microwave Sensing (MWS) can be used as a contactless detection method, which is particularly useful when the food is already packaged. In this paper we propose using MWS combined with Machine Learning (ML). In particular, we report on experiments we did with packaged cocoa-hazelnut spread and show the accuracy of our approach. We also present an FPGA acceleration that runs the ML processing in real-time so as to keep up with the throughput of a production line.
- Subjects :
- Production line
Support vector machines
Sensors
Computer science
Fpga acceleration
business.industry
Ranging
Machine learning
computer.software_genre
Microwave imaging, Microwave theory and techniques, Training, Support vector machines, Pollution measurement, Sensors, Plastics
Support vector machine
Microwave imaging
Microwave theory and techniques
Training
Artificial intelligence
business
Plastics
Throughput (business)
computer
Pollution measurement
Microwave
Food contaminant
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2020 IEEE International Symposium on Circuits and Systems (ISCAS)
- Accession number :
- edsair.doi.dedup.....03046f254c735c81974e6ae7c72f43f5