Near infrared spectroscopy and multivariate analysis as an effective, fast and cost-effective method to discriminate between Candida auris and Candida haemulonii

• Ano de defesa: 2024
• Autor(a) principal: Nascimento, Ayrton Lucas Firmino do
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Rio Grande do Norte; Brasil; UFRN; PROGRAMA DE PÓS-GRADUAÇÃO EM QUÍMICA
• Palavras-chave em Português: Química; Fungos patogénicos; Espectroscopia no infravermelho próximo; Análise multivariada; PCA-LDA; SPA-LDA; CNPQ::CIENCIAS EXATAS E DA TERRA::QUIMICA
• Link de acesso: https://repositorio.ufrn.br/handle/123456789/60732

Candida auris and Candida haemulonii are two emerging opportunistic pathogenic species that have been increasing in clinical cases worldwide in recent years. Differentiating some Candida species can be very laborious and needs very trained personnel, financially costly, tends to take day for a result and may not lead to results with very sensitivity and specificity, depending on their similarity. Thus, the objective of this study is to develop a new, faster and cost-effective methodology, compared with the standard techniques, for differentiating between C. auris and C. haemulonii based on near-infrared spectroscopy (NIR) and multivariate analysis. The strains C. auris CBS10913 and C. haemulonii CH02 were separated in 15 plates per species and three isolated colonies of each plate were selected for Fourier Transform Near-Infrared (FT-NIR) analysis, totaling 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% sensitivity and specificity for the discriminations. The SPA-LDA and GA-LDA algorithms were essential in selecting the most important variables (infrared wavelengths) for the models, which could be attributed to the overtone and combination bands of axial and angular deformation generated by functional groups present in the cell wall structures of these organisms, as polysaccharides, peptides, proteins or molecules resulting 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.