Detection and speciation of Arcobacter bacteria using Raman spectroscopy
department or school: Land and Food Systems, Faculty of
data provider: DSpace
degree campus: UBCV
creator: Wang, Kaidi
issued: 2019-08-29T16:32:43Z
issued: 2019
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: 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.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/71517
note: DETECTION AND SPECIATION OF ARCOBACTER BACTERIA USING RAMAN SPECTROSCOPY by Kaidi Wang B.Sc., Zhejiang University, 2017 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Food Science) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) August 2019 © Kaidi Wang, 2019 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, a thesis/dissertation entitled: Detection and speciation of Arcobacter bacteria using Raman spectroscopy submitted by Kaidi Wang in partial fulfillment of the requirements for the degree of Master of Science in Food Science Examining Committee: Xiaonan Lu Supervisor Keng Chou Supervisory Committee Member Supervisory Committee Member Carolina Tropini Additional Examiner Additional Supervisory Committee Members: Supervisory Committee Member Supervisory Committee Member iii Abstract 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…
has type: Thesis/Dissertation
date issued: 2019-11
is shown at: 10.14288/1.0380696
language: eng
degree discipline: Food Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/71517