Articles | Volume 4, issue 1
Geosci. Instrum. Method. Data Syst., 4, 45–56, 2015
Geosci. Instrum. Method. Data Syst., 4, 45–56, 2015

Research article 27 Feb 2015

Research article | 27 Feb 2015

COSIMA data analysis using multivariate techniques

J. Silén1, H. Cottin2, M. Hilchenbach3, J. Kissel3, H. Lehto4, S. Siljeström5, and K. Varmuza6 J. Silén et al.
  • 1Finnish Meteorological Institute, Erik Palmenin aukio 1, PB 503, 00101 Helsinki, Finland
  • 2Laboratoire Interuniversitaire des Systèmes Atmosphériques (LISA), UMR7583 – CNRS, Université Paris Est – Créteil (UPEC), Université Paris Diderot (UPD), 61 Avenue du Général de Gaulle, 94010 Créteil, France
  • 3Max Planck Institute for Solar System Research Justus-von-Liebig-Weg 3, 37077 Göttingen, Germany
  • 4Tuorla Observatory Dept of Physics and Astronomy University of Turku, 21500 Piikkiö, Finland
  • 5Department of Chemistry, Materials and Surfaces, SP Technical Research Institute of Sweden, Borås, Sweden
  • 6Vienna University of Technology, Department of Statistics and Probability Theory, Wiedner Hauptstrasse 7/107, 1040 Vienna, Austria

Abstract. We describe how to use multivariate analysis of complex TOF-SIMS (time-of-flight secondary ion mass spectrometry) spectra by introducing the method of random projections. The technique allows us to do full clustering and classification of the measured mass spectra. In this paper we use the tool for classification purposes. The presentation describes calibration experiments of 19 minerals on Ag and Au substrates using positive mode ion spectra. The discrimination between individual minerals gives a cross-validation Cohen κ for classification of typically about 80%. We intend to use the method as a fast tool to deduce a qualitative similarity of measurements.

Short summary
COSIMA, an advanced TOF-SIMS instrument measuring the mass spectrum of dust grains collected at comet P67 by the ROSETTA spacecraft, is predicted to encounter complex mixtures of minerals and organic compounds. To extract information from this data set, we have developed a multivariate technique tested on laboratory measurements made by an identical instrument under controlled conditions. We have shown that minerals can be identified and separated with high level of confidence.