Spectral imaging allows users to collect data without prior knowledge of the sample composition. The downside of spectral imaging is that large data sets are produced and extracting the important information can be difficult. One approach for reducing the data is to use principal component analysis [1] which extracts the underlying chemical components. An alternate approach is to use automatic clustering algorithms [2] which classifies the data into groups.