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CentWave Resolver


Feature detection → Chromatogram resolving → CentWave resolver

This method uses wavelets (from xcms library) to detect features within a chromatogram. A series of wavelets of different scales is convolved within the chromatogram. Local maxima in the convolution results determine the locations of possible peaks.

When these candidate feature locations co-occur at multiple scales then the scale with the strongest response indicates peak width. Given the candidate feature locations and scales, features can then be reconstructed from the original chromatogram.

Full details of the algorithm are published in Tautenhahn et al. [1].


The Wavelets detector employs Bioconductor's XCMS package for R [2]. Therefore, you must have R v2.15 or later installed. To install the XCMS package, run R and issue the following commands:


To run R from MZmine the Rserve package [3] must be installed in R, so also run the following R command:



  1. Ralf Tautenhahn, Christoph Böttcher, and Steffen Neumann "Highly sensitive feature detection for high resolution LC/MS" BMC Bioinformatics 2008, 9:504

  2. Bioconductor XCMS "LC/MS and GC/MS Data Analysis"

  3. Rserve "A TCP/IP server which allows other programs to use facilities of R"



This string is added to feature list name as suffix

Original feature list

Defines the processing.

Standard is to KEEP the original feature list and create a new processed list.

REMOVE saves memory.

PROCESS IN PLACE is an advanced option to process directly in the feature list and reduce memory consumption more - this might come with side effects, apply with caution.

MS/MS scan pairing

Set MS/MS scan pairing parameters. For more details see MS2 scan pairing

S/N Threshold

Features with a signal-to-noise ratio less than the threshold will be rejected.

The S:N ratio is defined as

\[(max - baseline) / sd\]

where max is the maximum feature intensity, baseline is the estimated baseline value, and sd is the standard deviation of local chromatographic noise.

Peak duration range

The acceptable range of feature widths. Features with widths outside this range will be rejected.

Peak integration method

Type of data used during feature reconstruction.

When reconstructing a feature from the chromatogram, gradient descent is used. This can be performed on the raw peak data or a smoothed version of it. Smoothed data is obtained through mexican hat filtering.

Using the unfiltered data is more accurate but can be susceptible to noise. The smooth data provide less exact results but are more robust in the presence of noise.

R engine

The R engine to be used for communicating with R. RServe might provide you with better performance.

Min # of data points

Minimum number of data points on a feature.


Last update: November 24, 2022 21:13:07