Implementation of machine learning study in Staphylococcus aureus’s FTIR spectra to antibiotic resistance identification

Claudia P. Barrera-Patiño; Jennifer M. Soares; Kate C. Blanco; Natalia M. Inada; Vanderlei Salvador Bagnato

doi:10.1117/12.3001639

12 March 2024 Implementation of machine learning study in Staphylococcus aureus’s FTIR spectra to antibiotic resistance identification

Claudia P. Barrera-Patiño, Jennifer M. Soares, Kate C. Blanco, Natalia M. Inada, Vanderlei Salvador Bagnato

Author Affiliations +

Proceedings Volume 12822, Photonic Diagnosis, Monitoring, Prevention, and Treatment of Infections and Inflammatory Diseases 2024; 1282202 (2024) https://doi.org/10.1117/12.3001639
Event: SPIE BiOS, 2024, San Francisco, California, United States

Abstract

Antibiotic is one of the most important medical inventions in the 20th century ¹. However, bacterial resistance to antibiotics is becoming a global health-care problem ². One of the important measures to tackle this problem is fast detection bacterial antibiotic susceptibility ¹. In this research topic and inspired by the work report of Soares et. al. ^3,4 we were motivated to developed this study to identification of resistance to antibiotic in Staphylococcus aureus. By mean of machine learning implementation in data analyses of Fourier-Transform Infrared Spectroscopy (FTIR) spectra, we found promisor results in samples with and without antibiotic resistance develop.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Claudia P. Barrera-Patiño, Jennifer M. Soares, Kate C. Blanco, Natalia M. Inada, and Vanderlei Salvador Bagnato "Implementation of machine learning study in Staphylococcus aureus’s FTIR spectra to antibiotic resistance identification", Proc. SPIE 12822, Photonic Diagnosis, Monitoring, Prevention, and Treatment of Infections and Inflammatory Diseases 2024, 1282202 (12 March 2024); https://doi.org/10.1117/12.3001639

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