Rapid Identification of MRSA Using Surface Enhanced Raman Scattering and Support Vector Machine


Uysal Çiloğlu F., Tokmakçı M. , Kahraman M., Korkmaz A., Kılıç İ. H. , Aydın Ö.

15th Nanoscience and Nanotechnology Conference, Antalya, Türkiye, 3 - 06 Kasım 2019, ss.97

  • Basıldığı Şehir: Antalya
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.97

Özet

Rapid and reliable identification of bacteria will decrease infectious diseases and antimicrobial resistance which has become a growing worldwide problem in recent years. Conventional methods which are used for diagnosis of bacterial infections are time-consuming, required trained analysts, and include complex sample preparation processes. Surface Enhanced Raman Scattering (SERS) is a powerful technique for rapid, sensitive and label free detection of biological samples. Metallic nanoparticles which are used in SERS technique enhance localized electric fields and thus the intensity of Raman scattering light increases significantly. In this study, we introduced the application of SERS combined with machine learning technique to classify 10 different subtypes of methicillin resistant Staphylococcus aureus (MRSA) species which are resistant to antibiotic at different levels. SERS spectra were collected using 785 nm laser excitation source, after each strain of MRSA was treated with Ag nanoparticle colloids to ensure signal enhancement. Raw spectra were preprocessed to remove noises from different sources by using some smoothing algorithms. Spectral dataset which includes 10 classes of MRSA consists of 92 observations and 1015 features. Principal component analysis (PCA) was applied to dataset in order to reduce feature space dimension. Support vector machine (SVM) is an elegant and powerful classification algorithm and gives better results than other conventional machine learning techniques. SVM together with PCA has clearly separated the classes each other with %95.86, %96.6, %99.4 accuracy, sensitivity, and specificity, respectively. The obtained results are highly promising for the application of Raman spectroscopy combined with machine learning in the determination of MRSA strains