Differentiation of mixed soil-borne fungi in the genus level using infrared spectroscopy and multivariate analysis

Abstract

Early detection of soil-borne pathogens, which have a negative effect on almost all agricultural crops, is crucial for effective targeting with the most suitable antifungal agents and thus preventing and/or reducing their severity. They are responsible for severe diseases in various plants, leading in many cases to substantial economic losses. In this study, infrared (IR) spectroscopic method, which is known as sensitive, accurate and rapid, was used to discriminate between different fungi in a mixture was evaluated. Mixed and pure samples of Colletotrichum, Verticillium, Rhizoctonia, and Fusarium genera were measured using IR microscopy. Our spectral results showed that the best differentiation between pure and mixed fungi was obtained in the 675–1800 cm−1 wavenumber region. Principal components analysis (PCA), followed by linear discriminant analysis (LDA) as a linear classifier, was performed on the spectra of the measured classes. Our results showed that it is possible to differentiate between mixed-calculated categories of phytopathogens with high success rates (~100%) when the mixing percentage range is narrow (40–60) in the genus level; when the mixing percentage range is wide (10–90), the success rate exceeded 85%. Also, in the measured mixed categories of phytopathogens it is possible to differentiate between the different categories with ~100% success rate.