Classification of toxigenic and atoxigenic strains of Aspergillus flavus with hyperspectral imaging

Aspergillus flavus (A. flavus) produces secondary metabolites, aflatoxins, that are harmful to both humans and animals. Because of stringent federal regulation requirements as well as the limitations of available detection methods, there is an urgent need for rapid, non-invasive and effective techniques such as hyperspectral imaging, for the detection of the toxigenic strains of A. flavus. Hyperspectral images of toxigenic and atoxigenic strains of A. flavus were classified. Principal component analysis (PCA) was applied for data decorrelation and dimensionality reduction. A Genetic Algorithm (GA) was implemented for the selection of principal components (PCs) based on Bhattacharya Distance (B-Distance). A Support Vector Machine (SVM) was successfully applied for the classification. Under halogen light sources, in average 83% of the toxigenic fungus pixels and 74% of the atoxigenic fungus pixels were correctly classified; while under UV light sources, 67% of the toxigenic fungus pixels and 85% of the atoxigenic fungus pixels were correctly classified. The pair-wise classification accuracies between toxigenic AF13 and each atoxigenic fungus species (AF38, AF283 and AF2038) were 80%, 91% and 95% under halogen light sources, and 75%, 97% and 99% under UV lights, respectively.