Shortwave Infrared Hyperspectral Imaging to Detect Contaminants in the U.S. Food Supply.

Abstract

The U.S. Food and Drug Administration (FDA) ensures the safety of the nation's food supply using sampling and laboratory analysis of imported and domestic foods. Accurate detection and identification of extraneous filth elements in inspected food samples is critical in producing evidence for regulatory decision-making. As part of ongoing efforts to increase the efficiency and accuracy of data collection, to better inform regulatory decision-making, scientists at the FDA have been exploring the application of emerging imaging technologies. To this end, we tested the ability of shortwave infrared (SWIR) hyperspectral image analysis to simultaneously detect and identify filth elements from a variety of chemically digested single- and multiple-ingredient food matrices. We tested five stored-product beetle species on a background of four different food matrix types. Our analyses successfully detected whole beetles and fragments as small as 0.65 mm in 95% of samples. All beetle species tested were accurately detected from the background matrices, and initial classification results show identification to genus. Our results show that SWIR spectral image analysis is a very promising technology for application in the detection and identification of filth elements in food products in a regulatory context and further development has the potential to increase analytical efficiency at FDA regulatory labs.