Off-campus UMass Amherst users: To download campus access dissertations, please use the following link to log into our proxy server with your UMass Amherst user name and password.

Non-UMass Amherst users: Please talk to your librarian about requesting this dissertation through interlibrary loan.

Dissertations that have an embargo placed on them will not be available to anyone until the embargo expires.

Author ORCID Identifier

https://orcid.org/0000-0002-3879-9271

AccessType

Open Access Dissertation

Document Type

dissertation

Degree Name

Doctor of Philosophy (PhD)

Degree Program

Food Science

Year Degree Awarded

2021

Month Degree Awarded

September

First Advisor

Lili He

Subject Categories

Analytical Chemistry | Food Microbiology

Abstract

The spread of antibiotic resistant bacteria around the world has become a major public health issue, and it is essential that effective detection methods exist for identifying these organisms and preventing them from spreading throughout our food systems and into the environment. The goal of this research is to develop a novel analytical procedure that is capable of easily identifying antibiotic resistance in bacterial samples, and also provides more information about the biochemical characteristics of the bacteria and their responses to antibiotic exposure. Surface-enhanced Raman Spectroscopy (SERS), an analytical technique that uses light scattering to produce a spectrum based on the chemical composition of a sample, was used as the basis for this protocol. First, several different SERS-based procedures were evaluated for their effectiveness in this application, using ampicillin-sensitive and resistant E. coli O157:H7 as a model organism. These included a conventional method in which the bacteria were simply mixed with gold nanoparticles and analyzed, as well as more novel approaches for analyzing the extracellular matrix liquid of the bacteria and using SERS-based filter mapping. Each of these methods were found to have potential advantages and disadvantages, but the latter two approaches were found to be particularly promising for future work. After these tests, we worked to develop a filter- based SERS protocol that could be used with a portable Raman spectrometer, which would be much more suitable for future practical applications. Additional antibiotics, including neomycin and chlortetracycline, were also evaluated, and our portable SERS method was found to be effective for evaluating bacterial sensitivity to each of these antibiotics. Our SERS procedure was also tested with bacteria samples isolated from ground beef, and was able to correctly assess their antibiotic sensitivity. Next, we worked to optimize an extracellular matrix liquid-based analysis method that could be used with a portable Raman spectrometer, which requires additional testing and optimization steps to design compared to the filter-based method. A variety of different experimental conditions were tested, which also provided valuable information about the origins of particular SERS patterns observed in the samples and the conditions required to observe them. The optimized portable SERS liquid analysis procedure would allow us to avoid several labor-intensive and time-consuming sample preparation steps that are required for the previously developed SERS approaches, and our method was be used effectively with a variety of different bacterial samples. Finally, we successfully tested our liquid-based portable SERS procedure with antibiotic resistant bacteria isolated from supermarket poultry samples. Our research demonstrates that SERS can be an efficient and accurate method for testing the antibiotic sensitivity. Potential future work includes testing more types of food samples to assess the potential differences in the SERS patterns of their bacteria isolates, as well as conducting further in-depth research into testing mixed bacterial populations and analyzing the development of antibiotic resistance in a sample over time.

DOI

https://doi.org/10.7275/24358226

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Share

COinS