Authentication and Traceability of Food Products through the Supply Chain Using NQR Spectroscopy
Title | Authentication and Traceability of Food Products through the Supply Chain Using NQR Spectroscopy |
Publication Type | Conference Paper |
Year of Publication | 2017 |
Authors | Zhang, F., Masna, N. V. R., Bhunia, S., Chen, C., Mandal, S. |
Conference Name | 2017 IEEE Biomedical Circuits and Systems Conference (BioCAS) |
Keywords | authentication, Capacitors, Chemicals, Food products, Human Behavior, pubcrawl, resilience, Resiliency, Scalability, Spectroscopy, supply chain security, Supply chains, Support vector machines |
Abstract | Maintaining the security and integrity of our food supply chain has emerged as a critical need. In this paper, we describe a novel authentication approach that can significantly improve the security of the food supply chain. It relies on applying nuclear quadrupole resonance (NQR) spectroscopy to authenticate the contents of packaged food products. NQR is a non-invasive, non-destructive, and quantitative radio frequency (RF) spectroscopic technique. It is sensitive to subtle features of the solid-state chemical environment such that signal properties are influenced by the manufacturing process, thus generating a manufacturer-specific watermark or intrinsic tag for the product. Such tags enable us to uniquely characterize and authenticate products of identical composition but from different manufacturers based on their NQR signal parameters. These intrinsic tags can be used to verify the integrity of a product and trace it through the supply chain. We apply a support vector machine (SVM)-based classification approach that trains the SVM with measured NQR parameters and then authenticates food products by checking their test responses. Measurement on an example substance using semi-custom hardware shows promising results (95% classification accuracy) which can be further improved with improved instrumentation. |
URL | https://ieeexplore.ieee.org/document/8325173/ |
DOI | 10.1109/BIOCAS.2017.8325173 |
Citation Key | zhang_authentication_2017 |