Corn is commonly used as a good source of food and feed, as well as for producing cooking oil and starch. However, corn is among the many agricultural staples that can be easily contaminated with aflatoxin, a poisonous mycotoxin produced by molds that can have serious effects on human and animal health, and rapid and effective methods for detecting aflatoxin in the corn are lacking for on-site use in food processing operations. This study investigated the use of short-wavelength infrared (900 - 2500 nm) hyperspectral image data for detecting aflatoxin in ground maize, using measurements of aflatoxin content via chemical analysis for sample reference. Preliminary results are reported for the development of a detection model using deep learning to detect aflatoxin-contaminated corn powder.
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