UV resonance Raman spectroscopy as an identification tool for pathogenic Candida samples

NUR Abstract von Poster P8: Vibrational spectroscopic techniques like, e.g. IR absorption or Raman spectroscopy are alternatives to conventional microbiological identification methods. Vibrational spectroscopy monitors all cell components and is therefore a phenotypic method, requiring a standardization of the cultivation conditions [1]. A special type of Raman spectroscopy is resonance Raman spectroscopy utilizing electronic resonant excitation wavelengths like e.g. in the deep ultraviolet region (UV) at 244 nm. The application of UV resonance Raman (UVRR) spectroscopy leads to a selective enhancement of the Raman signals of taxonomic important macromolecules like e.g. DNA/RNA bases and aromatic amino acids up to six orders of magnitude. This resonant enhancement yields simplified UV resonance Raman spectra showing a higher sensitivity and specifity as compared to normal non-resonant Raman spectra with respect to classification/identification of bacteria [2,3]. Furthermore, the fluorescence background in UV resonance Raman spectra is negligible for excitation at wavelengths below 260 nm, leading to Raman spectra with a high signal to noise ratio. In this contribution, we build up a database of 2370 UV resonance Raman spectra of 11 different Candida species (C. albicans, C. dubliniensis, C. glabrata, C. guilliermondii, C. krusei, C. tropicalis, C. viswanathii, C. parapsilosis, C. kefir, C. lusitaniae and C. africana) with various strains for the identification of pathogenic Candida samples. The established database resulted in a classification accuracy of 99.9%. This database has been used for an identification of 1139 UV resonance Raman spectra of independently grown yeast samples. In doing so an accuracy of 94.4% could be achived.