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ARS Home » Southeast Area » New Orleans, Louisiana » Southern Regional Research Center » Food and Feed Safety Research » Research » Publications at this Location » Publication #297923
Title: Support vector machines classification of fluorescence hyperspectral image for detection of aflatoxin in corn kernels

Author
item SAMIAPPAN, SATHISHKUMAR - Mississippi State University
item BRUCE, LORI - Mississippi State University
item YAO, HAIBO - Mississippi State University
item HRUSKA, ZUZANA - Mississippi State University
item Brown, Robert
item Bhatnagar, Deepak
item Cleveland, Thomas

Submitted to: Meeting Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 5/1/2013
Publication Date: 6/28/2013
Citation: Samiappan, S., Bruce, L.M., Yao, H., Hruska, Z., Brown, R.L., Bhatnagar, D., Cleveland, T.E. 2013. Support vector machines classification of fluorescence hyperspectral image for detection of aflatoxin in corn kernels. In: Proceedings of the 5th IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Conference, June 25-28, 2013, Gainesville, FL. p. 1-4.

Interpretive Summary:

Technical Abstract: Aflatoxin contamination is a real concern for all classes of livestock. They are produced by certain mold fungi, Aspergillus flavus and Aspergillus parasiticus. Aflatoxin in food is hazardous for humans and animals. In this work, we propose a non-invasive system for detecting aflatoxin and classifying corn kernels based on the aflatoxin contamination levels. Fluorescence hyperspectral images of single corn kernels were used for experiments. Single and multi-classifier configurations of support vector machines are used to classify single corn kernels on a per-pixel basis. The performance of SVM classification with and without feature selection is assessed. Confusion matrices of different configurations are used for comparison, demonstrating that the multi-classifier system with non-uniform feature selection performs well, achieving an overall accuracy of 84%.