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Proceedings Paper

Classification of corn kernels contaminated with aflatoxins using fluorescence and reflectance hyperspectral images analysis
Author(s): Fengle Zhu; Haibo Yao; Zuzana Hruska; Russell Kincaid; Robert Brown; Deepak Bhatnagar; Thomas Cleveland
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Paper Abstract

Aflatoxins are secondary metabolites produced by certain fungal species of the Aspergillus genus. Aflatoxin contamination remains a problem in agricultural products due to its toxic and carcinogenic properties. Conventional chemical methods for aflatoxin detection are time-consuming and destructive. This study employed fluorescence and reflectance visible near-infrared (VNIR) hyperspectral images to classify aflatoxin contaminated corn kernels rapidly and non-destructively. Corn ears were artificially inoculated in the field with toxigenic A. flavus spores at the early dough stage of kernel development. After harvest, a total of 300 kernels were collected from the inoculated ears. Fluorescence hyperspectral imagery with UV excitation and reflectance hyperspectral imagery with halogen illumination were acquired on both endosperm and germ sides of kernels. All kernels were then subjected to chemical analysis individually to determine aflatoxin concentrations. A region of interest (ROI) was created for each kernel to extract averaged spectra. Compared with healthy kernels, fluorescence spectral peaks for contaminated kernels shifted to longer wavelengths with lower intensity, and reflectance values for contaminated kernels were lower with a different spectral shape in 700-800 nm region. Principal component analysis was applied for data compression before classifying kernels into contaminated and healthy based on a 20 ppb threshold utilizing the K-nearest neighbors algorithm. The best overall accuracy achieved was 92.67% for germ side in the fluorescence data analysis. The germ side generally performed better than endosperm side. Fluorescence and reflectance image data achieved similar accuracy.

Paper Details

Date Published: 13 May 2015
PDF: 6 pages
Proc. SPIE 9488, Sensing for Agriculture and Food Quality and Safety VII, 94880M (13 May 2015); doi: 10.1117/12.2176578
Show Author Affiliations
Fengle Zhu, Mississippi State Univ. (United States)
Geosystems Research Institute (United States)
Haibo Yao, Mississippi State Univ. (United States)
Geosystems Research Institute (United States)
Zuzana Hruska, Mississippi State Univ. (United States)
Geosystems Research Institute (United States)
Russell Kincaid, Mississippi State Univ. (United States)
Geosystems Research Institute (United States)
Robert Brown, Agricultural Research Service (United States)
Deepak Bhatnagar, Agricultural Research Service (United States)
Thomas Cleveland, Agricultural Research Service (United States)


Published in SPIE Proceedings Vol. 9488:
Sensing for Agriculture and Food Quality and Safety VII
Moon S. Kim; Kuanglin Chao; Bryan A. Chin, Editor(s)

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