Multimode Hyperspectral Imaging for Food Quality and Safety By Fartash Vasefi, Nicholas Booth, Hesam Hafizi and Daniel L. Farkas
Food safety and quality are becoming progressively important, and a failure to implement monitoring processes and identify anomalies in composition, production, and distribution can lead to severe financial and customer health damages. If consumers were uncertain about food safety and quality, the impact could be profound; hence, we need better ways of minimizing such risks. On the data management side, the rise of artificial intelligence, data analytics, the Internet of Things, and blockchain all provide enormous opportunities for supply chain management and liability management, but the impact of any approach starts with the quality of the relevant data. Here, we present state-of-the-art spectroscopic technologies including hyperspectral reflectance, fluorescence imaging as well as Raman spectroscopy, and speckle imaging that are all validated for food safety and quality applications. We believe a multimode approach comprising of a number of these synergetic optical detection modes is needed for the highest performance. We present a plan where our implementations reflect this concept through a multimode tabletop system in the sense that a large, real-time production-level device would be based on more modes than this mid-level one, while a handheld, portable unit may only address fewer challenges, but with a lower cost and size.