Automated classification of food products using 2D low-field NMR
Mason Greer1, Cheng Chen1, Soumyajit Mandal1
1Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, USA.
Abstract:
In this work, low-field proton (1H) and sodium (23Na) relaxation and diffusion measurements are used to detect and classify different types of food products. A compact and low-cost system based on a small 0.5 T permanent magnet has been developed to autonomously authenticate such products. The system uses a simple but efficient double-tuned matching network suitable for 1H/23Na NMR. Various machine learning algorithms are used to classify food samples based on T1-T2 and D-T2 data generated by the system, and the accuracy and prediction speed of these algorithms are studied in detail. The influence of temperature drift upon prediction accuracy is also studied. Experimental results demonstrate reliable classification of cooking oils, milk, and soy sauces.
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