Hostname: page-component-586b7cd67f-vdxz6 Total loading time: 0 Render date: 2024-11-26T14:58:08.578Z Has data issue: false hasContentIssue false
  • English
  • Français

Improved Background Removal Method Using Principal Components Analysis for Spatially Resolved Electron Energy Loss Spectroscopy

Published online by Cambridge University Press:  28 January 2005

Niclas Borglund ,
Per-Gustav Åstrand  and
Stefan Csillag
Niclas Borglund
Affiliation:
Stockholm University, Department of Physics, S-106 91 Stockholm, Sweden
Per-Gustav Åstrand
Affiliation:
Stockholm University, Department of Physics, S-106 91 Stockholm, Sweden
Stefan Csillag
Affiliation:
Stockholm University, Department of Physics, S-106 91 Stockholm, Sweden
Get access

Abstract

Principal components analysis (PCA) factor filtering is implemented for the improvement of background removal in noisy spectra. When PCA is used as a method for filtering before background removal in electron energy loss spectroscopy elemental maps, an improvement in the accuracy of the background fit with very short fitting intervals is achieved, leading to improved quality of elemental maps from noisy spectra. This opens the possibility to use shorter exposure times for elemental mapping, leading to fewer problems with, for example, drift and beam damage.

Keywords

electron energy loss spectroscopybackground removalprincipal components analysis
Type
INSTRUMENTATION AND TECHNIQUES
Information
Microscopy and Microanalysis , Volume 11 , Issue 1 , February 2005 , pp. 88 - 96
Copyright
© 2005 Microscopy Society of America

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Bevington, P.R. & Robinson, D.K. (1992). Data Reduction and Error Analysis for Physical Scienses, 2nd ed. New York: McGraw-Hill Inc.
Bonnet, N., Brun, N., & Colliex, C. (1999). Extracting information from sequences of spatially resolved EELS spectra using multivariate statistical analysis. Ultramicroscopy 77, 97–112.Google Scholar
Bonnet, N., Colliex, C., Mory, C., & Tencé, M. (1988). Developments in processing image sequences for elemental mapping. Scanning Micros Suppl. 2, 351–364.Google Scholar
Brun, R. & Rademakers, F. (1997). ROOT—An Object Oriented Data Analysis Framework. Nuclear Instrum Methods Phys Res A 389, 81–86.Google Scholar
Egerton, R.F. (1996). Electron Energy Loss Spectroscopy for the Electron Microscope, 2nd ed. New York: Plenum Press.
Jeanguillaume, C. & Colliex, C. (1989). Spectrum-image—The next step in EELS digital acquisition and processing. Ultramicroscopy 28, 252.Google Scholar
Orsholm, C., Borglund, N., & Csillag, S. (2000). High detective quantum efficiency fast electron detector for electron energy loss spectroscopy. Micron 31, 441–450.Google Scholar
Ottensmeyer, F.P. & Frankland, B.W. (1988). Spectral processing for parallel recording of elemental maps. Scanning Micros Suppl. 2, 343.Google Scholar
Trebbia, P. & Bonnet, N. (1990). EELS elemental mapping with unconventional methods, I. Theoretical basis: Image analysis with multivariate statistics and entropy concepts. Ultramicroscopy 34, 165–178.Google Scholar
Wind, H. (1984). Principal component analysis and its applications to track finding. In Formulae and Methods in Experimental Data Evaluation, Vol. 3, pp. K1–K16. Geneva: European Physical Society.