Microchimica Acta

, Volume 128, Issue 3–4, pp 241–250 | Cite as

The wavelet transform: A new preprocessing method for peak recognition of infrared spectra

  • Frank Ehrentreich
  • Stavri G. Nikolov
  • Martin Wolkenstein
  • Herbert Hutter
Original Papers


The wavelet transform, also called wavelet decomposition, recently introduced into the applied sciences and available as software packages, is a powerful method for smoothing experimental data. The wavelet transform is a mathematical transform for hierarchically decomposing functions. It leads to a description of a function, including discrete data vectors or matrices, in terms of a coarse overall shape and details of a graded sequence. This decomposition is the basis for noise reduction. At the various levels of decomposition the coarse coefficients are due to the characteristic signals and part of the details may be interpreted as noise. The method will be discussed on examples of peak recognition in infrared spectroscopy. We will show that some of the wavelet bases lead to a very good compromise between signal/noise ratio enhancement and preservation of the real data structures. Subsequently it enables a Teak ‘Picker’ to find the local maxima of the curve corresponding to real data structures.

Key words

infrared spectroscopy peak recognition data preprocessing smoothing wavelet transform 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    J. Zupan,Algorithms for Chemists, Wiley, Chichester, 1989.Google Scholar
  2. [2]
    A. Savitzky, M. J. E. Golay,Anal. Chem.,1964,36, 1627.Google Scholar
  3. [3]
    D. L. Donoho,Proc. Symp. Appl. Math. 1993,47, 173.Google Scholar
  4. [4]
    S. G. Mallat,IEEE Trans. PAMI 1989,11, 674.Google Scholar
  5. [5]
    R. Salzer,Technical Report, TU Dresden, 1995.Google Scholar
  6. [6]
    MATLAB 4.2b for Windows, The Mathworks Inc., Natick, MA, USA.Google Scholar
  7. [7]
    D. Donoho,Teach Wave 0.550, Stanford University, USA.Google Scholar
  8. [8]
    B. Vidakovic, P. Muller, internet-manuscript,, 1991.Google Scholar
  9. [9]
    M. A. Cody, internet-manuscript,≈mcody/ papers.html, 1992.Google Scholar
  10. [10]
    A. Graps, internet-manuscript, papers/, 1995.Google Scholar
  11. [11]
    D. L. Donoho,Technical Report No. 416, Department of Statistics, Stanford University, CA, USA, 1993.Google Scholar
  12. [12]
    O. Rioul, M. Vetterli,IEEE SP Magazine,1991, 15.Google Scholar
  13. [13]
    E. J. Stollnitz, T. D. DeRose, D. H. Salesin,IEEE Comput. Graph. Appl,1995, 76.Google Scholar
  14. [14]
    E. J. Stollnitz, T. D. DeRose, D. H. Salesin,IEEE Comput. Graph. Appl,1995, 75.Google Scholar
  15. [15]
    J. Bradley, C. Brislawn, T. Hopper,‘The FBI wavelet/scalar quantization standard for gray-scale fingerprint image compression’, Tech. Report LA-UR-93-1659, Los Alamos National Lab, Los Alamos, N. M., USA, 1993.Google Scholar
  16. [16]
    S. G. Mallat, W.-L. Hwang,IEEE Trans. Inform. Theory 1992,38, 617.Google Scholar
  17. [17]
    W. H. Press, S. A. Teukolsky, W. T. Vetterling, B. P. Flannery,Numerical Recipes in C, 2nd ed., Cambridge University Press, Cambridge, 1992.Google Scholar
  18. [18]
    D. L. Donoho,Technical Report No. 409, Department of Statistics, Stanford University, CA, USA, 1992.Google Scholar
  19. [19]
    SpecInfo, Chemical Concepts, Weinheim, Germany.Google Scholar

Copyright information

© Springer-Verlag 1998

Authors and Affiliations

  • Frank Ehrentreich
    • 1
  • Stavri G. Nikolov
    • 2
  • Martin Wolkenstein
    • 2
  • Herbert Hutter
    • 2
  1. 1.Institut für Analytische ChemieTU Bergakademie FreibergFreibergGermany
  2. 2.Institut für Analytische ChemieTechnische Universität WienWienAustria

Personalised recommendations