Date of Award

Summer 8-7-2024

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Mathematics and Statistics

First Advisor

Yi Jiang

Second Advisor

Eric Gilbert

Third Advisor

Gary Hastings

Fourth Advisor

Zhongshan Li

Abstract

Antimicrobial resistance (AMR) poses a significant threat to global health, with Methicillin-resistant Staphylococcus aureus (MRSA) contributing substantially to morbidity. Rapid identification of MRSA versus Methicillin-susceptible S. aureus (MRSA) is critical for timely and appropriate antibiotic use. This study explores the use of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) on Fourier Transform Infrared (FTIR) spectroscopy data to quickly distinguish MRSA from MSSA. By analyzing the growth patterns and spectral data of SA6538 (MSSA) and SA43300 (MRSA) under antibiotic stress, we demonstrate the feasibility of separation, highlighting spectral differences and their likely biological causes. LDA, when applied to primary, secondary, and tertiary FTIR datasets, achieves high classification accuracy, particularly when initially processed with PCA. This combined approach suggests a rapid and reliable diagnostic method to improve clinical outcomes and curb the spread of AMR.

DOI

https://doi.org/10.57709/37395645

File Upload Confirmation

1

Download

Share

COinS