Classification of corn kernels contaminated with aflatoxins using fluorescence and reflectance hyperspectral images analysis

Fengle Zhu; Haibo Yao; Zuzana Hruska; Russell Kincaid; Robert Brown; Deepak Bhatnagar; Thomas Cleveland

doi:10.1117/12.2176578

13 May 2015 Classification of corn kernels contaminated with aflatoxins using fluorescence and reflectance hyperspectral images analysis

Fengle Zhu, Haibo Yao, Zuzana Hruska, Russell Kincaid, Robert Brown, Deepak Bhatnagar, Thomas Cleveland

Author Affiliations +

Proceedings Volume 9488, Sensing for Agriculture and Food Quality and Safety VII; 94880M (2015) https://doi.org/10.1117/12.2176578
Event: SPIE Sensing Technology + Applications, 2015, Baltimore, MD, United States

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.

Citation Download Citation

Fengle Zhu, Haibo Yao, Zuzana Hruska, Russell Kincaid, Robert Brown, Deepak Bhatnagar, and Thomas Cleveland "Classification of corn kernels contaminated with aflatoxins using fluorescence and reflectance hyperspectral images analysis", Proc. SPIE 9488, Sensing for Agriculture and Food Quality and Safety VII, 94880M (13 May 2015); https://doi.org/10.1117/12.2176578

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