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.

Abstract-We present a method for clustering the speakers from unlabeled and unsegmented conversation (with known number of speakers), when no a priori knowledge about the identity of the participants is given. Each speaker was modeled by a self-organizing map (SOM). The SOMs were randomly initiated. An iterative algorithm allows the data move from one model to another and adjust the SOMs. The restriction that the data can move only in small groups but not by moving each and every feature vector separately force the SOMs to adjust to speakers (instead of phonemes or other vocal events). This method was applied to high-quality conversations with two to five participants and to two-speaker telephone-quality conversations. The results for two (both high-and telephone-quality) and three speakers were over 80% correct segmentation. The problem becomes even harder when the number of participants is also unknown. Based on the iterative clustering algorithm a validity criterion was also developed to estimate the number of speakers. In 16 out of 17 conversations of high-quality conversations between two and three participants, the estimation of the number of the participants was correct. In telephone-quality the results were poorer.

Fusarium is a large fungi genus of a large variety of species and strains which inhabits soil and vegetation. It is distributed worldwide and affiliated to both warm and cold weather. Fusarium oxysporum species, for instance, cause the Fusarium wilt disease of plants, which appears as a leaf wilting, yellowing and eventually plant death. Early detection and identification of these pathogens are very important and might be critical for their control. Previously, we have managed to differentiate among different fungi genera (Rhizoctonia, Colletotrichum, Verticillium and Fusarium) using FTIR-ATR spectroscopy methods and cluster analysis. In this study, we used Fourier-transform infrared (FTIR) attenuated total reflection (ATR) spectroscopy to discriminate and differentiate between different strains of F. oxysporum. The result obtained was of spectral patterns distinct to each of the various examined strains, which belong to the same species. These differences were not as significant as those found between the different genera species. We applied advanced statistical techniques: principal component analysis (PCA) and linear discriminant analysis (LDA) on the FTIR-ATR spectra in order to examine the feasibility of distinction between these fungi strains. The results are encouraging and indicate that the FTIR-ATR methodology can differentiate between the different examined strains of F. oxysporum with a high success rate. Based on our PCA and LDA calculations performed in the regions [900-1775 cm(-1), 2800-2990 cm(-1), with 9 PCs], we were able to classify the different strains with high success rates: Foxy1 90%, Foxy2 100%, Foxy3 100%, Foxy4 92.3%, Foxy5 83.3% and Foxy6 100%.

Colletotrichum coccodes (C. coccodes) is a pathogenic fungus which causes anthracnose on tomatoes and black dot disease in potatoes. It is important to differentiate among these isolates and to detect the origin of newly discovered isolates, in order to treat the disease in its early stages. However, distinguishing between isolates using common biological methods is time-consuming, and not always available. We used Fourier Transform Infra-Red (FTIR)-Attenuated Total Reflectance (ATR) spectroscopy and advanced mathematical and statistical methods to distinguish between different isolates of C. coccodes. To our knowledge, this is the first time that FTIR-ATR spectroscopy was used, combined with multivariate analysis, to classify such a large number of 15 isolates belonging to the same species. We obtained a success rate of approximately 90% which was achieved using the region 800-1775 cm(-1). In addition we succeeded in determining the relative spectral similarity between different fungal isolates by developing a new algorithm. This method could be an important potential diagnostic tool in agricultural research, since it may outline the extent of the biological similarity between fungal isolates. Based on the PCA calculations, we grouped the fifteen isolates included in this study into four different degrees of similarity.

Klebsiella pneumoniae (K. pneumoniae) is one of the most aggressive multidrug-resistant bacteria associated with human infections, resulting in high mortality and morbidity. We obtained 1190 K. pneumoniae isolates from different patients with urinary tract infections. The isolates were measured to determine their susceptibility regarding nine specific antibiotics. This study's primary goal is to evaluate the potential of infrared spectroscopy in tandem with machine learning to assess the susceptibility of K. pneumoniae within approximately 20 min following the first culture. Our results confirm that it was possible to classify the isolates into sensitive and resistant with a success rate higher than 80% for the tested antibiotics. These results prove the promising potential of infrared spectroscopy as a powerful method for a K. pneumoniae susceptibility test.