Methods
Salman, A., Department of Physics, SCE-Sami Shamoon College of Engineering, Beer-Sheva 84100, Israel
Shufan, E., Department of Physics, SCE-Sami Shamoon College of Engineering, Beer-Sheva 84100, Israel
Tsror, L., Department of Plant Pathology, Institute of Plant Protection, Agricultural Research Organization, Gilat Research Center, M.P. Negev 85250, Israel
Moreh, R., Department of Physics, Ben-Gurion University, Beer-Sheva 84105, Israel
Mordechai, S., Department of Physics, Ben-Gurion University, Beer-Sheva 84105, Israel
Huleihel, M., Department of Microbiology, Immunology and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
In this study the potential of infrared (IR) spectroscopy for the classification of Colletotrichum coccodes (C. coccodes) isolates into vegetative compatibility groups (VCGs) was evaluated. Isolates which belong to the same VCG may have similar pathological and physiological traits that differ from those that are not assigned to the same VCG. Early classification of isolates into VCGs is of a great importance for a better understanding of the epidemiology of the disease and improves its control. The main goal of the present study was to classify 14 isolates of C. coccodes into VCGs and differentiate between them, based on their IR absorption spectra as obtained by the FTIR-ATR sampling technique. Advanced statistical and mathematical methods, including Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), were applied to the spectra after manipulation. The results show that it is possible to assign the isolates into VCGs with more than 90% success based on the wavenumber low region (1800-800cm-1) and using 15 PCs. However, on the isolate level, the best differentiation results were obtained using PCA (15 PCs) and LDA for the combined regions (2990-2800cm-1, 1800-800cm-1), with identification success rates of 87.2%. © 2014.
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הספר "אוצר וולקני"
אודות
תנאי שימוש
Classification of Colletotrichum coccodes isolates into vegetative compatibility groups using infrared attenuated total reflectance spectroscopy and multivariate analysis
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Salman, A., Department of Physics, SCE-Sami Shamoon College of Engineering, Beer-Sheva 84100, Israel
Shufan, E., Department of Physics, SCE-Sami Shamoon College of Engineering, Beer-Sheva 84100, Israel
Tsror, L., Department of Plant Pathology, Institute of Plant Protection, Agricultural Research Organization, Gilat Research Center, M.P. Negev 85250, Israel
Moreh, R., Department of Physics, Ben-Gurion University, Beer-Sheva 84105, Israel
Mordechai, S., Department of Physics, Ben-Gurion University, Beer-Sheva 84105, Israel
Huleihel, M., Department of Microbiology, Immunology and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
Classification of Colletotrichum coccodes isolates into vegetative compatibility groups using infrared attenuated total reflectance spectroscopy and multivariate analysis
In this study the potential of infrared (IR) spectroscopy for the classification of Colletotrichum coccodes (C. coccodes) isolates into vegetative compatibility groups (VCGs) was evaluated. Isolates which belong to the same VCG may have similar pathological and physiological traits that differ from those that are not assigned to the same VCG. Early classification of isolates into VCGs is of a great importance for a better understanding of the epidemiology of the disease and improves its control. The main goal of the present study was to classify 14 isolates of C. coccodes into VCGs and differentiate between them, based on their IR absorption spectra as obtained by the FTIR-ATR sampling technique. Advanced statistical and mathematical methods, including Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), were applied to the spectra after manipulation. The results show that it is possible to assign the isolates into VCGs with more than 90% success based on the wavenumber low region (1800-800cm-1) and using 15 PCs. However, on the isolate level, the best differentiation results were obtained using PCA (15 PCs) and LDA for the combined regions (2990-2800cm-1, 1800-800cm-1), with identification success rates of 87.2%. © 2014.
Scientific Publication