Use of Near-Infrared Reflectance Spectrometry and Multivariate Data Analysis to Detect Anther Smut Disease (Microbotryum violaceum) inSilene dioica

Near-infrared reflectance (NIR) spectral data was used in principal component analysis (PCA) to detect infection of Silene dioica by Microbotryum violaceum. Rosette leaf samples were accurately identified as either healthy (97%) or infected (96%) when NIR data was analyzed by PCA. The two classes overlapped slightly when principal component models were used to classify unknown samples. A method to measure the degree of infection is also presented. The use of NIR and PCA for both detection and quantification of fungal biomass in plant material should be useful for studying plant-pathogen interactions and as a method for assessing disease incidence in crops