Identification of methicillin-resistant Staphylococcus aureus bacteria using surface-enhanced Raman spectroscopy and machine learning techniques
To combat antibiotic resistance, it is extremely important to select the right antibiotic by performing rapid diagnosis of pathogens. Traditional techniques require complicated sample preparation and time-consuming processes that are not proper for rapid diagnosis. To address this problem, we used surface-enhanced Raman spectroscopy combined with machine learning techniques for rapid identification of Gram-positive Staphylococcus aureus methicillin-resistant and sensitive strains and Gram-negative Legionella pneumophila (control group). A total of 10 methicillin-resistant S. aureus (MRSA), 3 methicillin-sensitive S. aureus (MSSA) and 6 L. pneumophila isolates were used. Collected spectra indicated high reproducibility and repeatability with a high signal to noise ratio. Principal component analysis (PCA), hierarchical cluster analysis (HCA), and various supervised classification algorithms were performed to discriminate both S. aureus strains and L. pneumophila. Although there were not any noteworthy differences between MRSA and MSSA spectra with the naked eye, the ratio of some peaks intensity such as 732/958, 732/1333, and 732/1450 proved that there could be a significant indicator showing the difference between them. K-nearest neighbors (kNN) classification algorithm gave superior classification performance with 97.8% accuracy between the traditional classifiers including support vector machine (SVM), decision tree (DT), and naïve Bayes (NB). Our results indicate that SERS combined with machine learning can be used for the detection of antibiotic-resistant and susceptible bacteria and this technique is a very promising tool for clinical applications.