Co-Authors:
Salman, A., SCE-Sami Shamoon College of Engineering, Department of Physics, Beer-Sheva 84100, Israel
Lapidot, I., SCE-Sami Shamoon College of Engineering, Department of Electrical and Electronics Engineering, Beer-Sheva 84100, Israel
Pomerantz, A., Ben-Gurion University of the Negev, Department of Virology and Developmental Genetics, Faculty of Health Sciences, Beer-Sheva 84105, Israel
Tsror, L., Institute of Plant Protection, Department of Plant Pathology, Agricultural Research Organization, M.P. Negev, 85250, Israel
Shufan, E., SCE-Sami Shamoon College of Engineering, Department of Physics, Beer-Sheva 84100, Israel
Moreh, R., Ben-Gurion University, Department of Physics, Beer-Sheva 84105, Israel
Mordechai, S., Ben-Gurion University, Department of Physics, Beer-Sheva 84105, Israel
Huleihel, M., Ben-Gurion University of the Negev, Department of Virology and Developmental Genetics, Faculty of Health Sciences, Beer-Sheva 84105, Israel
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. © 2012 Society of Photo-Optical Instrumentation Engineers (SPIE).
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