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Title: Classification of corn kernels contaminated with aflatoxins using fluorescence and reflectance hyperspectral image analysis

item ZHU, FENGLE - Mississippi State University
item YAO, HAIBO - Mississippi State University
item HRUSKA, ZUZANA - Mississippi State University
item KINCAID, RUSSELL - Mississippi State University
item Brown, Robert
item Bhatnagar, Deepak
item Cleveland, Thomas

Submitted to: Proceedings of SPIE
Publication Type: Proceedings
Publication Acceptance Date: 3/23/2015
Publication Date: 3/23/2015
Citation: Zhu, F., Yao, H., Hruska, Z., Kincaid, R., Brown, R.L., Bhatnagar, D., Cleveland, T.E. 2015. Classification of corn kernels contaminated with aflatoxins using fluorescence and reflectance hyperspectral image analysis. In: Proceedings of SPIE, Sensing for Agriculture and Food Quality and Safety VIII, March 21-22, 2015, Baltimore, MD. p. 9488-22.

Interpretive Summary:

Technical 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 rapid 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 20 ppb threshold with K-nearest neighbors. 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.