Salmonella is a leading cause of foodborne illness. Traditional detection methods require lengthy incubation periods
or expensive reagent kits. Hyperspectral microscope images (HMIs) have been previously investigated as a method for
early and rapid detection of bacteria by using a spectral signature that is unique to the organism. Previous HMI use with
bacteria has consisted of supervised classification with hypercubes collected for single culture images isolated from
highly selective growth media. In order to move forward with HMI as a detection tool in the food industry, unsupervised
classification of bacteria cells in mixed culture HMIs was investigated. Four foodborne bacteria cultures, S. Typhimurium
(ST) E. coli (Ec), S. aureus (Sa) and L. innocua (Li) were combined in seven different culture combinations with HMIs
collected between 450 nm and 800 nm. A k-means divisive cluster analysis (CA) was implemented and mixed culture
image sets were found to contain between two and four clusters. CA cluster accuracy was obtained by assigning a
dummy variable of the proposed CA classification, then carrying out a discriminant analysis. From the mixed culture HMIs,
700 bacteria cells were classified and accuracies were between 91.92% and 100%, with six of the seven HMI sets
resulting in > 97% accuracies. A distance measure between clusters was applied to identify unknown clusters based
on single culture reference samples of the four bacteria used. Results showed that the CA has potential for unsupervised
classification of bacteria cells, but the distance metric was not an adequate method for identifying the unknown cluster
based on reference spectra, potentially due to the collinearity amongst bacteria spectra.