Unsupervised classification of individual foodborne bacteria from a mixture of bacteria cultures within a hyperspectral microscope image

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 forearly and rapid detection of bacteria by using a spectral signature that is unique to the organism. Previous HMI use withbacteria has consisted of supervised classification with hypercubes collected for single culture images isolated fromhighly selective growth media. In order to move forward with HMI as a detection tool in the food industry, unsupervisedclassification 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 HMIscollected between 450 nm and 800 nm. A k-means divisive cluster analysis (CA) was implemented and mixed cultureimage sets were found to contain between two and four clusters. CA cluster accuracy was obtained by assigning adummy 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 setsresulting in > 97% accuracies. A distance measure between clusters was applied to identify unknown clusters basedon single culture reference samples of the four bacteria used. Results showed that the CA has potential for unsupervisedclassification of bacteria cells, but the distance metric was not an adequate method for identifying the unknown clusterbased on reference spectra, potentially due to the collinearity amongst bacteria spectra.