Identification of Bacillus Spores Using Clustering of Principal Components of Fluorescence Data

We have measured the fluorescence spectra of three different bacterial spores (B. globigii, B. cereus, and B. popillae) mixed with one of three different samples of domestic dust and using five excitation wavelengths. The data are analyzed with principal component analysis and clustered with partition around medoids method. We are able to correctly identify each of the samples, even with the dust involved. We also measured a separate preparation of B. globigii, ovalbumin, and corn smut fungal spores. All the B. globigii samples showed excellent clustering. The ovalbumin and corn smut spores showed weak or ambiguous clustering with B. cereus. While fluorescence combined with principal component analysis and clustering techniques shows strong promise to correctly identify bacterial spores, additional work is necessary to discriminate the fluorescence signals from other biological samples.