Rapid Identification of Antibiotic Resistance in Staphylococcus Using FTIR and Machine Learning

Antimicrobial resistance (AMR) poses a significant threat to global health, with Methicillin-resistant <em>Staphylococcus aureus (MRSA)</em> contributing substantially to morbidity. Rapid identification of MRSA versus Methicillin-susceptible <em>S. aureus (MRSA)</em> 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) <em>under antibiotic stress</em>, 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.