The application of UV resonance Raman spectroscopy for the differentiation of clinically relevant Candida species - Silge, Anja; Heinke, Ralf; Bocklitz, Thomas; Wiegand, Cornelia; Hipler, Uta-Christina; Roesch, Petra; Popp, Jürgen (Analytical and Bioanalytical Chemistry, 2018)
Candida-related infections have become a major problem in hospitals. The species identification of yeast is the prerequisite for the initiation of adequate antifungal therapy. In the present study, the connection between inherent UV resonance Raman (RR) spectral profiles of Candida species and taxonomic differences was investigated for the first time. UV RR in combination with statistical modeling was applied to extract taxonomic information from the spectral fingerprints for subsequent differentiation. The identification accuracies of independent batch cultures were determined by applying a leave-one-batch-out cross validation. The quality of differentiation can be divided into three levels. Within a defined taxonomic group comprising the species C. glabrata, C. guilliermondii, and C. haemulonii, the identification accuracy was low. On the next level, the identification results of C. albicans and C. tropicalis were characterized by high sensitivities of 98 and 95% but simultaneously challenged by false-positive predictions due to the misallocation of C. spherica (as C. albicans) and C. viswanathii (as C. tropicalis). The highest level of identification accuracies was reached for the species C. dubliniensis, C. krusei, C. africana, C. novergica, and C. parapsilosis. Reliable identification results were observed with accuracies ranging from 93 up to 100%. The species allocation based on the UV RR spectral profiles could be reproduced by the identification of independent batch cultures. We conclude that the introduced spectroscopic approach is capable of transforming the high-dimensional UV RR data of Candida species into clinically useful decision parameters. [Figure not available: see fulltext.].
CRIS-Team Scopus Importer:2023-04-20
Keywords: Candida albicans; Identification; Leave-one-batch-out cross validation; Multivariate statistics; UV resonance Raman spectroscopy