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Claudia Beleites
Researcher at Leibniz Institute of Photonic Technology
Publications - 35
Citations - 1810
Claudia Beleites is an academic researcher from Leibniz Institute of Photonic Technology. The author has contributed to research in topics: Raman spectroscopy & Hyperspectral imaging. The author has an hindex of 19, co-authored 33 publications receiving 1548 citations. Previous affiliations of Claudia Beleites include Dresden University of Technology & University of Trieste.
Papers
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Sample size planning for classification models.
TL;DR: The test sample sizes necessary to achieve reasonable precision in the validation of classifier training and testing are determined and it is found that 75-100 samples will usually be needed to test a good but not perfect classifier.
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Disease recognition by infrared and Raman spectroscopy.
TL;DR: The current review gives an overview of the experimental techniques, data‐classification algorithms and applications to assess soft tissues, hard tissues and body fluids to recognize various diseases.
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Chemical imaging of articular cartilage sections with Raman mapping, employing uni- and multi-variate methods for data analysis
Alois Bonifacio,Claudia Beleites,Franco Vittur,Eleonora Marsich,Sabrina Semeraro,Sergio Paoletti,Valter Sergo +6 more
TL;DR: Partial least squares regression model gives a semiquantitative mapping of the biochemical constituents in agreement with average composition found in the literature, and the combination of hierarchical and fuzzy cluster analysis succeeds in detecting variations between different regions of the extra-cellular matrix.
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Methodology for fiber-optic raman mapping and FTIR imaging of metastases in mouse brains
Christoph Krafft,Christoph Krafft,Matthias Kirsch,Claudia Beleites,Gabriele Schackert,Reiner Salzer +5 more
TL;DR: To optimize the preparation of pristine brain tissue to obtain reference information, to optimize the conditions for introducing a fiber-optic probe to acquire Raman maps, and to transfer previous results obtained from human brain tumors to an animal model.
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Poly-L-lysine-coated silver nanoparticles as positively charged substrates for surface-enhanced Raman scattering.
TL;DR: Positively charged nanoparticles to be used as substrates for surface-enhanced Raman scattering (SERS) were prepared by coating citrate-reduced silver nanoparticles with the cationic polymer poly-l-lysine, allowing quantitative analysis of bilirubin aqueous solutions.