AUTHOR=Nascimento Ayrton L. F. , de Medeiros Anthony G. J. , Neves Ana C. O. , de Macedo Ana B. N. , Rossato Luana , Assis Santos Daniel , dos Santos André L. S. , Lima Kássio M. G. , Bastos Rafael W. 

TITLE=Near-infrared spectroscopy and multivariate analysis as effective, fast, and cost-effective methods to discriminate Candida auris from Candida haemulonii

JOURNAL=Frontiers in Chemistry

VOLUME=Volume 12 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/chemistry/articles/10.3389/fchem.2024.1412288

DOI=10.3389/fchem.2024.1412288

ISSN=2296-2646

ABSTRACT=Candida auris and Candida haemulonii are two emerging opportunistic pathogens that have been growing in clinical cases in the last years worldwide. The differentiation of some Candida species turns to be very laborious, difficult, financially costly and time consuming depending on the similarity between them. Thus, the objective of this work is to develop a new, faster and less expensive methodology for differentiating between C. auris vs. C. haemulonii based on near-infrared spectroscopy (NIR) and multivariate analysis. C. auris CBS10913 and C. haemulonii CH02 were separated in 15 plates per specie and three isolated colonies of each plate were selected for Fourier Transform Near-Infrared (FT-NIR) analysis, totalizing 90 spectra. Subsequently, Principal Component Analysis (PCA) and variable selection algorithms, including the Successive Projections Algorithm (SPA) and Genetic Algorithm (GA) coupled with Linear Discriminant Analysis (LDA), were employed to discern distinctive patterns among the samples. The use of PCA, SPA and GA algorithms associated with LDA achieved 100% of both sensitivity and specificity for the discriminations. The SPA-LDA and GA-LDA algorithms were essential in selecting the variables (infrared wavelengths) of most importance for the models, which could be attributed to binding of cell wall structures as polysaccharides, peptides, proteins or molecules resultant from yeasts' metabolism. These results show the high potential of combined FT-NIR and multivariate analysis techniques for the classification of Candida-like fungi, which can contribute to faster and more effective diagnosis and treatment of patients affected by these microorganisms.