| Summary: | Hyperspectral microscope images (HMIs) have been previously explored as a tool for the early and rapid detection of common foodborne pathogenic bacteria. A robust unsupervised classification approach to differentiate bacterial species with the potential for single cell sensitivity is needed for real-world application, in order to confirm the identity of pathogenic bacteria isolated from a food product. Here, a one-class soft independent modelling of class analogy (SIMCA) was used to determine if individual cells are <i>Salmonella</i> positive or negative. The model was constructed and validated with a spectral library built over five years, containing 13 <i>Salmonella</i> serotypes and 14 non-<i>Salmonella</i> foodborne pathogens. An image processing method designed to take less than one minute paired with the one-class <i>Salmonella</i> prediction algorithm resulted in an overall classification accuracy of 95.4%, with a <i>Salmonella</i> sensitivity of 0.97, and specificity of 0.92. SIMCA’s prediction accuracy was only achieved after a robust model incorporating multiple serotypes was established. These results demonstrate the potential for HMI as a sensitive and unsupervised presumptive screening method, moving towards the early (<8 h) and rapid (<1 h) identification of <i>Salmonella</i> from food matrices.
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