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Comparison of multivariate classification algorithms using EEM fluorescence data to distinguish Cryptococcus neoformans and Cryptococcus gattii pathogenic fungi

Abstract

Cryptococcus neoformans and Cryptococcus gattii are the etiologic agents of cryptococcosis, whose suitable treatment depends of rapid and correct detection and differentiation of the Cryptococcus specie. Currently, this identification is made by classical and molecular techniques, however most of them are considered laborious and expensive. As an alternative method to discriminate C. gattii and C. neoformans, excitation-emission matrix (EEM) fluorescence spectroscopy combined with the multivariate classification methods: Unfolded Partial Least Squares Discriminant Analysis (UPLS-DA), multiway- Partial Least Squares Discriminant Analysis (nPLS-DA), Parallel Factor Analysis (PARAFAC), Principal Component Analysis (PCA), Successive Projection Algorithm (SPA) and Genetic Algorithm (GA), followed by Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) were herein investigated. This technique showed to be an innovative and low expensive methodology which requires a small sample volume. Among the results, the most successful model was the UGA-LDA, which showed a sensitivity of 88.9 % within only 5 selected wavelengths in calibration and 100.0% prediction for both classes of C. neoformans and C. gattii, equaling and or bettering some of the biological tests that are usually carried out to differentiate these fungi.

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Publication details

The article was received on 23 Mar 2017, accepted on 05 Jun 2017 and first published on 06 Jun 2017


Article type: Paper
DOI: 10.1039/C7AY00781G
Citation: Anal. Methods, 2017, Accepted Manuscript
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    Comparison of multivariate classification algorithms using EEM fluorescence data to distinguish Cryptococcus neoformans and Cryptococcus gattii pathogenic fungi

    F. S. Costa, P. Silva, C. Lelis, R. Theodoro, T. Arantes and K. M. G. de Lima, Anal. Methods, 2017, Accepted Manuscript , DOI: 10.1039/C7AY00781G

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