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A Machine-Learning Based Microwave Sensing Approach to Food Contaminant Detection | IEEE Conference Publication | IEEE Xplore

A Machine-Learning Based Microwave Sensing Approach to Food Contaminant Detection


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...Show More

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.
Date of Conference: 12-14 October 2020
Date Added to IEEE Xplore: 28 September 2020
Print ISBN:978-1-7281-3320-1
Print ISSN: 2158-1525
Conference Location: Seville, Spain

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