Identification of fungal phytopathogens using Fourier transform infrared-attenuated total reflection spectroscopy and advanced statistical methods

Abstract

The early diagnosis of phytopathogens is of a great importance; it could save large economical losses due to crops damaged by fungal diseases, and prevent unnecessary soil fumigation or the use of fungicides and bactericides and thus prevent considerable environmental pollution. In this study, 18 isolates of three different fungi genera were investigated; six isolates of Colletotrichum coccodes, six isolates of Verticillium dahliae and six isolates of Fusarium oxysporum. Our main goal was to differentiate these fungi samples on the level of isolates, based on their infrared absorption spectra obtained using the Fourier transform infrared-attenuated total reflection (FTIR-ATR) sampling technique. Advanced statistical and mathematical methods: principal component analysis (PCA), linear discriminant analysis (LDA), and k-means were applied to the spectra after manipulation. Our results showed significant spectral differences between the various fungi genera examined. The use of k-means enabled classification between the genera with a 94.5% accuracy, whereas the use of PCA [3 principal components (PCs)] and LDA has achieved a 99.7% success rate. However, on the level of isolates, the best differentiation results were obtained using PCA (9 PCs) and LDA for the lower wavenumber region (800-1775 cm(-1)), with identification success rates of 87%, 85.5%, and 94.5% for Colletotrichum, Fusarium, and Verticillium strains, respectively.

Distinction of fungal polysaccharides by N/C ratio and mid infrared spectroscopy

“…However, for quantitative application, it requires choosing an appropriate calibration based on an absolute method like NMR and depends on the characteristic and internal reference bands as well as on baselines necessary for measuring the intensity of absorption. On the other hand, FTIR spectroscopy combined with multivariate statistical methods has been applied to identification and discrimination of various biological samples, including spores and/or mycelia of fungal phytopathogens [44][45][46][47], fruiting bodies of cultivated mushrooms [48], wood degraded by fungi [49], yeast cells [50] and cell walls [51], based on their chemical composition. Based on the information mentioned above, we suggest that such combination of FTIR and chemometry is suitable for rapid screening analysis of fungal cell wall derived materials in accordance with the relationship between chitin (chitosan) and ␤-d-glucans and in comparison with pure polysaccharides.…”

Study of plasma-induced peripheral blood mononuclear cells survival using Fourier transform infrared microspectroscopy

“…Examination of the results was performed using the variant "leave-one-out" (LOO), 25,26 (usually applied with small amount of data) when k ¼ N, the number of data points.…”

Characterization of Phytophthora infestans resistance to mefenoxam using FTIR spectroscopy

“…FTIR has high sensitivity, good resolution, and good signal-to-noise ratio (SNR) and is simple to use, quick, and economical [8,9]. Since the 1990s FTIR spectroscopy has been used for the examination of biological samples, taking advantage of its ability to measure very small samples [10][11][12][13][14][15]. Since the early 2010s phytopathogenic fungi FTIR investigation began to rise in popularity, leading to successful classification and differentiation among phytopathogenic fungi genera species and isolates [15] .…”

“…Since the 1990s FTIR spectroscopy has been used for the examination of biological samples, taking advantage of its ability to measure very small samples [10][11][12][13][14][15]. Since the early 2010s phytopathogenic fungi FTIR investigation began to rise in popularity, leading to successful classification and differentiation among phytopathogenic fungi genera species and isolates [15] . These successes were enhanced by combining FTIR with advanced mathematical methods such as Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA) [13,14].…”

“…The number of points in each spectrum is of the order of hundreds. Separating between the classes working with hundreds of points for each measurement can be significantly improved if the number of dimensions is reduced by using the PCA method[15][16][17].In this work the use of PCA enables the reduction of the number of dimensions from a few hundred to several dozen, keeping enough information for the category separation. Data reduction is obtained by choosing a partial set of PCs.…”