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Author Alfeld, M.; Wahabzada, M.; Bauckhage, C.; Kersting, K.; van der Snickt, G.; Noble, P.; Janssens, K.; Wellenreuther, G.; Falkenberg, G. pdf  doi
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  Title Simplex Volume Maximization (SiVM): a matrix factorization algorithm with non-negative constrains and low computing demands for the interpretation of full spectral X-ray fluorescence imaging data Type A1 Journal article
  Year 2017 Publication Microchemical journal Abbreviated Journal Microchem J  
  Volume 132 Issue Pages 179-184  
  Keywords A1 Journal article; AXES (Antwerp X-ray Analysis, Electrochemistry and Speciation)  
  Abstract Technological progress allows for an ever-faster acquisition of hyperspectral data, challenging the users to keep up with interpreting the recorded data. Matrix factorization, the representation of data sets by bases (or loads) and coefficient (or score) images is long used to support the interpretation of complex data sets. We propose in this publication Simplex Volume Maximization (SiVM) for the analysis of X-ray fluorescence (XRF) imaging data sets. SiVM selects archetypical data points that represents the data set and thus provides easily understandable bases, preserves the non-negative character of XRF data sets and has low demands concerning computing resources. We apply SiVM on an XRF data set of Hans Memling's Portrait of a man from the Lespinette family from the collection of the Mauritshuis (The Hague, NL) and discuss capabilities and shortcomings of SiVM. (C) 2017 Elsevier B.V. All rights reserved.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Wos 000399845700026 Publication Date 2017-02-04  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0026-265x; 0026-265x ISBN Additional Links UA library record; WoS full record; WoS citing articles  
  Impact Factor 3.034 Times cited 8 Open Access  
  Notes ; The German Federal Ministry of Education and Research (BMBF) is acknowledged for the financial support (Verbundprojekt 05K2012 POISSON: Fortschrittliche Faktorenanalyse ffir Poisson-verteilte Daten). ; Approved (down) Most recent IF: 3.034  
  Call Number UA @ admin @ c:irua:152647 Serial 5830  
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Author Alvarez-Martin, A.; Kavich, G. pdf  doi
openurl 
  Title SPME-GC–MS for the off-gassing analysis of a complex museum object Type A1 Journal article
  Year 2021 Publication Microchemical Journal Abbreviated Journal Microchem J  
  Volume 167 Issue Pages 106276  
  Keywords A1 Journal article; Engineering sciences. Technology; AXES (Antwerp X-ray Analysis, Electrochemistry and Speciation); Antwerp X-ray Imaging and Spectroscopy (AXIS)  
  Abstract The identification of volatile organic compounds (VOCs) emitted by a complex museum object, composed of materials of different nature, has been optimized by solid-phase microextraction coupled to gas chromatography-mass spectrometry (SPME-GC–MS). The performance of two fiber coatings and four sampling times were tested and compared in order to define the best sampling conditions. The method allowed a fair extraction of volatile and semivolatile compounds emitted naturally by the object, without any type of accelerating aging. In addition, on-fiber derivatization was applied to improve the extraction efficiency and reduce the sampling time of harmful carboxylic acids emitted by the object. The results obtained are of prime importance to show the off-gassing activity of a valuable museum object in order to take further decisions related with its storage and display conditions.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Wos 000662640500001 Publication Date 2021-04-21  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0026-265x; 0026-265x ISBN Additional Links UA library record; WoS full record  
  Impact Factor 3.034 Times cited Open Access Not_Open_Access  
  Notes Approved (down) Most recent IF: 3.034  
  Call Number UA @ admin @ c:irua:181924 Serial 8577  
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