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.

A Machine-Learning Based Microwave Sensing Approach to Food Contaminant Detection / Urbinati, Luca; Ricci, Marco; Turvani, Giovanna; Vasquez, Jorge A. Tobon; Vipiana, Francesca; Casu, Mario R.. - (2020), pp. 1-5. ((Intervento presentato al convegno 2020 IEEE International Symposium on Circuits and Systems (ISCAS) tenutosi a Seville nel 10-21 Oct. 2020 [10.1109/ISCAS45731.2020.9181293].

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

Urbinati, Luca;Ricci, Marco;Turvani, Giovanna;Vasquez, Jorge A. Tobon;Vipiana, Francesca;Casu, Mario R.
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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2871836