Métodos espectroscópicos e classificação multivariada aplicados na diferenciação de microrganismos patógenos

• Ano de defesa: 2019
• Autor(a) principal: Costa, Fernanda Saadna Lopes da
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Brasil; UFRN; PROGRAMA DE PÓS-GRADUAÇÃO EM QUÍMICA
• Palavras-chave em Português: FT-IR; Fluorescência; Análise multivariada; Cryptococcus; Klebsiella sp. e Escherichia coli; CNPQ::CIENCIAS EXATAS E DA TERRA::QUIMICA
• Link de acesso: https://repositorio.ufrn.br/jspui/handle/123456789/28084

This paper presents the development of multivariate classification methods, combined with spectroscopic techniques, such as spectroscopy in the middle infrared region and molecular fluorescence, in the detection of pathogenic microorganisms: fungi and bacteria. The first studies sought the differentiation of Cryptococcus neoformans and Cryptococcus gattii. These fungi are the etiological agents of cryptococcosis, whose proper treatment depends on the rapid and correct detection and differentiation of the species. This identification is currently done by classical and molecular techniques, which are mostly laborious and expensive. As an alternative method to discriminate C. gattii and C. neoformans, we initially investigated medium infrared spectroscopy by attenuated total reflectance, together with multivariate classification techniques (PCALDA/QDA, GA-LDA/QDA, SPA-LDA/QDA), in which the GA-QDA model obtained sensitivity in classes C. neoformans and C. gattii of 84.4% and 89.3%, respectively, using only 17 wave numbers. Then, fluorescence spectroscopy in excitation-emission matrix (EEM) was used, combined with multivariate classification methods (UPCA-LDA/QDA, UGA-LDA/QDA, USPA-LDA/QDA, PARAFAC/PLS-DA, nPLS-DA). The most satisfactory model was the UGA-LDA, which used only 5 wavelengths, and showed sensitivity of 88.9% in calibration and 100.0% prediction for both species, results that are comparable to routine biological tests. The last study aimed to differentiate sensitive and multi-resistant bacteria of the genera Klebsiella sp. and Escherichia coli. Through molecular fluorescence spectroscopy and multivariate classification, methods LDA, QDA and SVM coupled with data reduction algorithms PCA, GA and SPA. Among these, the models with the best performance for both types of bacteria presented sensitivity and specificity rates of 100%. Compared to the classical methods, the methodologies proposed in these studies proved to be an innovative, faster and cheaper alternative for the identification of pathogenic microorganisms, such as fungi and bacteria, opening the possibility of application in routine diagnostic laboratories.