Development of classification model for detecting aflatoxin on ground maize based on SWIR hyperspectral imaging.

Insuck Baek; Yong-Kyoung Kim; Moon Kim

doi:10.1117/12.2622396

30 May 2022 Development of classification model for detecting aflatoxin on ground maize based on SWIR hyperspectral imaging.

Insuck Baek, Yong-Kyoung Kim, Moon Kim

Author Affiliations +

Proceedings Volume PC12120, Sensing for Agriculture and Food Quality and Safety XIV; PC121200A (2022) https://doi.org/10.1117/12.2622396
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Insuck Baek, Yong-Kyoung Kim, and Moon Kim "Development of classification model for detecting aflatoxin on ground maize based on SWIR hyperspectral imaging.", Proc. SPIE PC12120, Sensing for Agriculture and Food Quality and Safety XIV, PC121200A (30 May 2022); https://doi.org/10.1117/12.2622396

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