How To Deal With Noise

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Overview

There are several strategies available to deal with noise in chromatographic and mass spectrometric data. Removing noise usually intends to increase the signal-to-noise ratio or to improve the peak shapes for subsequent peak detection. Typical techniques are smoothing, basline correction or background subtraction. OpenChrom offers a couple of filters to reduce noise.

Subtract background

If you want to remove a user-selected background from your detected peaks, retention time windows or your entire chromatograms, check out the workflow described here.

Remove noisy or unimportant ions

If you want to remove ions from your mass spectrometric data you can apply the implemented filter "Ion Remover" Before applying the Ion Remover you are advised to adjust the settings, i.e. to select the distinct ions or even broad m/z ranges to be removed. Navigate to the Preferences and select the settings page for the "Ion Remover".



Smooth chromatograms

Automated noise removal techniques

There are several filters available which are designed to remove noise from your chromatograms in an automated way:

  • Backfolding Filter: This noise reduction algorithm was adapted from the one described in the following papers:
    • Ghosh, A., & Anderegg, R. J. (1989). Differential gas chromatographic mass spectrometry. Analytical Chemistry, 61, 73–77.
    • Pool, W. G., de Leeuw, J. W., & van de Graaf, B. (1996). Backfolding Applied to Differential Gas Chromatography/Mass Spectrometry as a Mathematical Enhancement of Chromatographic Resolution. Journal of Mass Spectrometry, 31, 509–516.
    • Pool, W. G., De Leeuw, J. W., & Van de Graaf, B. (1997). Automated extraction of pure mass spectra from gas chromatographic/mass spectrometric data. Journal Of Mass Spectrometry, 32, 438–443.
    • Pool, W. G., Maas, L. R. M., de Leeuw, J. W., & van de Graaf, B. (1997). Automated Processing of GC/MS Data : Quantification of the Signals of Individual Components. Journal of Mass Spectrometry, 32, 1253–1257.


  • CODA Filter: This noise reduction algorithm was adapted from the one described in the following paper:
    • Windig, W., Phalp, J. M., & Payne, A. W. (1996). A Noise and Background Reduction Method for Component Detection in Liquid Chromatography/Mass Spectrometry. Analytical Chemistry, 68(20), 3602–3606. doi:10.1021/ac960435y


  • Denoising Filter: This noise reduction algorithm was adapted from the one described in the following paper:
    • Wenig, P. (2011). Post-optimization of Py-GC/MS data: a case study using a new digital chemical noise reduction filter (NOISERA) to enhance the data quality utilizing OpenChrom mass spectrometric software. Journal of Analytical and Applied Pyrolysis, 92, 202-208. http://dx.doi.org/10.1016/j.jaap.2011.05.013