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UBC Theses and Dissertations

Detection and speciation of Arcobacter bacteria using Raman spectroscopy Wang, Kaidi


Rabid and accurate identification of Arcobacter species is of great importance because these bacteria have been considered as emerging foodborne pathogens and potential zoonotic agents. Raman spectroscopy has the ability to differentiate bacteria based upon Raman scattering spectral features of bacterial whole cells, which is fast, reagentless, and easy to perform. Thus, we aimed to detect and discriminate Arcobacter at the species level using confocal micro-Raman spectroscopy (785 nm) coupled with chemometric analysis. A total of 82 isolates of 18 Arcobacter species from clinical, environmental and agri-food sources in both Canada and Germany were included. The genus Arcobacter could be successfully differentiated from closely related genera Campylobacter and Helicobacter using Raman spectroscopy via a principal component analysis model. We also determined that bacterial cultivation time and temperature did not significantly influence the spectral reproducibility and the discrimination capability of Raman spectroscopy. For the identification of Arcobacter to the species level, an overall accuracy of 94.13% was achieved for all 18 Arcobacter species by using Raman spectroscopy in combination with machine learning using a convolutional neural network. Furthermore, a back-propagation neural network was constructed to determine the actual ratio of a specific Arcobacter species in a bacterial mixture ranging from 5% to 100% by biomass with an accuracy of over 99%. Finally, Raman spectroscopy showed the ability to detect trace level (10°-10¹ CFU/mL) of Arcobacter from food sample (i.e., milk) after enrichment. The knowledge received from this study can be applied to further investigate the epidemiology of Arcobacter in the food chain.

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