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%.