Discrimination of waterborne pathogens, Cryptosporidium parvum oocysts and bacteria using surface-enhanced Raman spectroscopy coupled with principal component analysis and hierarchical clustering

Arslan A. H., Ciloglu F., Yilmaz U., ŞİMŞEK E., AYDIN Ö.

Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy, cilt.267, 2022 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 267
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1016/j.saa.2021.120475
  • Dergi Adı: Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, CAB Abstracts, Chimica, Compendex, EMBASE, INSPEC, MEDLINE, Veterinary Science Database
  • Anahtar Kelimeler: Surface-enhanced Raman spectroscopy, Label-free detection, Waterborne pathogen, C, parvum, Principal component analysis, Hierarchical clustering, CRYPTOSPORIDIUM-PARVUM, LABEL-FREE, SCATTERING, IDENTIFICATION, SILVER, OUTBREAKS, PATHOGENS, ADSORPTION, SPECTRA, PROTEIN
  • Erciyes Üniversitesi Adresli: Evet

Özet

© 2021 Elsevier B.V.Waterborne pathogens (parasites, bacteria) are serious threats to human health. Cryptosporidium parvum is one of the protozoan parasites that can contaminate drinking water and lead to diarrhea in animals and humans. Rapid and reliable detection of these kinds of waterborne pathogens is highly essential. Yet, current detection techniques are limited for waterborne pathogens and time-consuming and have some major drawbacks. Therefore, rapid screening methods would play an important role in controlling the outbreaks of these pathogens. Here, we used label-free surface-enhanced Raman Spectroscopy (SERS) combined with multivariate analysis for the detection of C. parvum oocysts along with bacterial contaminants including, Escherichia coli, and Staphylococcus aureus. Silver nanoparticles (AgNPs) are used as SERS substrate and samples were prepared with simply mixed of concentrated AgNPs with microorganisms. Each species presented distinct SERS spectra. Principal component analysis (PCA) and hierarchical clustering were performed to discriminate C. parvum oocysts, E. coli, and S. aureus. PCA was used to visualize the dataset and extract significant spectral features. According to score plots in 3 dimensional PCA space, species formed distinct group. Furthermore, each species formed different clusters in hierarchical clustering. Our study indicates that SERS combined with multivariate analysis techniques can be utilized for the detection of C. parvum oocysts quickly.