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- ItemA Machine Learning-Based Raman Spectroscopic Assay for the Identification of Burkholderia mallei and Related Species(Basel : MDPI, 2019) Silge, Anja; Moawad, Amira A.; Bocklitz, Thomas; Fischer, Katja; Rösch, Petra; Roesler, Uwe; Elschner, Mandy C.; Popp, Jürgen; Neubauer, HeinrichBurkholderia (B.) mallei, the causative agent of glanders, and B. pseudomallei, the causative agent of melioidosis in humans and animals, are genetically closely related. The high infectious potential of both organisms, their serological cross-reactivity, and similar clinical symptoms in human and animals make the differentiation from each other and other Burkholderia species challenging. The increased resistance against many antibiotics implies the need for fast and robust identification methods. The use of Raman microspectroscopy in microbial diagnostic has the potential for rapid and reliable identification. Single bacterial cells are directly probed and a broad range of phenotypic information is recorded, which is subsequently analyzed by machine learning methods. Burkholderia were handled under biosafety level 1 (BSL 1) conditions after heat inactivation. The clusters of the spectral phenotypes and the diagnostic relevance of the Burkholderia spp. were considered for an advanced hierarchical machine learning approach. The strain panel for training involved 12 B. mallei, 13 B. pseudomallei and 11 other Burkholderia spp. type strains. The combination of top- and sub-level classifier identified the mallei-complex with high sensitivities (>95%). The reliable identification of unknown B. mallei and B. pseudomallei strains highlighted the robustness of the machine learning-based Raman spectroscopic assay. © 2019 by the authors
- ItemRaman imaging of changes in the polysaccharides distribution in the cell wall during apple fruit development and senescence(Berlin ; Heidelberg : Springer, 2016) Szymańska-Chargot, Monika; Chylińska, Monika; Pieczywek, Piotr M.; Rösch, Petra; Schmitt, Michael; Popp, Jürgen; Zdunek, ArturMain conclusion Du ring on-tree ripening, the pectin distribution changed from polydispersed in cell wall to cumulated in cell wall corners. During apple storage, the pectin distribution returned to evenly dispersed along the cell wall. The plant cell wall influences the texture properties of fruit tissue for example apples become softer during ripening and postharvest storage. This softening process is believed to be mainly connected with changes in the cell wall composition due to polysaccharides undergoing an enzymatic degradation. These changes in polysaccharides are currently mainly investigated via chemical analysis or monoclonal labeling. Here, we propose the application of Raman microscopy for evaluating the changes in the polysaccharide distribution in the cell wall of apples during both ripening and postharvest storage. The apples were harvested 1 month and 2 weeks before optimal harvest date as well as at the optimal harvest date. The apples harvested at optimal harvest date were stored for 3 months. The Raman maps, as well as the chemical analysis were obtained for each harvest date and after 1, 2 and 3 months of storage, respectively. The analysis of the Raman maps showed that the pectins in the middle lamella and primary cell wall undergo a degradation. The changes in cellulose and hemicellulose were less pronounced. These findings were confirmed by the chemical analysis results. During development changes of pectins from a polydispersed form in the cell walls to a cumulated form in cell wall corners could be observed. In contrast after 3 months of apple storage we could observe an substantial pectin decrease. The obtained results demonstrate that Raman chemical imaging might be a very useful tool for a first identification of compositional changes in plant tissue during their development. The great advantage Raman microspectroscopy offers is the simultaneous localization and identification of polysaccharides within the cell wall and plant tissue.