<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Meier, Mario Federico</dc:creator>
  <dc:creator>Mildenberger, Thoralf</dc:creator>
  <dc:creator>Locher, René</dc:creator>
  <dc:creator>Rausch, Juanita</dc:creator>
  <dc:creator>Zünd, Thomas</dc:creator>
  <dc:creator>Neururer, Christoph</dc:creator>
  <dc:creator>Ruckstuhl, Andreas</dc:creator>
  <dc:creator>Grobéty, Bernard</dc:creator>
  <dc:date>2018-09-01</dc:date>
  <dc:description xmlns:ns0="xml" ns0:lang="en">Computer controlled scanning electron microscopy (CCSEM) is a widely-used method  for single airborne particle analysis. It produces extensive chemical and morphological  data sets, whose processing and interpretation can be very time consuming. We  propose an automated two-stage particle classification procedure based on elemental  compositions of individual particles. A rule-based classifier is applied in the first stage  to form the main classes consisting of particles containing the same elements. Only  elements with concentrations above a threshold of 5 wt% are considered. In the  second stage, data of each main class are isometrically log-ratio transformed and  then clustered into subclasses, using a robust, model-based method. Single particles  which are too far away from any more densely populated region are excluded during  training, preventing these particles from distorting the definition of the sufficiently  populated subclasses. The classifier was trained with over 55,000 single particles  from 83 samples of manifold environments, resulting in 227 main classes and 465  subclasses in total. All these classes are checked manually by inspecting the ternary  plot matrix of each main class. Regardless of the size of training data, some particles  might belong to still undefined classes. Therefore, a classifier was chosen which can  declare particles as unknown when they are too far away from all classes defined  during training.</dc:description>
  <dc:format>application/pdf</dc:format>
  <dc:identifier>https://folia.unifr.ch/global/documents/307215</dc:identifier>
  <dc:identifier>https://folia.unifr.ch/documents/307215/files/gro_mbt.pdf</dc:identifier>
  <dc:identifier>https://folia.unifr.ch/documents/307215/files/gro_mbt_sm.pdf</dc:identifier>
  <dc:identifier>https://folia.unifr.ch/documents/307215/files/gro_mbt_sm3.pdf</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jaerosci.2018.05.012</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>License undefined</dc:rights>
  <dc:source>Journal of Aerosol Science. - 2018, vol. 123, p. 1–16</dc:source>
  <dc:subject>info:eu-repo/classification/udc/549</dc:subject>
  <dc:title xmlns:ns1="xml" ns1:lang="en">A model based two-stage classifier for airborne particles analyzed with Computer Controlled Scanning Electron Microscopy</dc:title>
  <dc:type>http://purl.org/coar/resource_type/c_6501</dc:type>
</oai_dc:dc>
