Windowed mass selection method: a new data processing algorithm for liquid chromatography–mass spectrometry data

Abstract A number of preprocessing methods are tested on liquid chromatography–mass spectrometry (LC–MS) peptide map data, to determine the best and most efficient way to improve the signal to noise ratio in the data, especially at low analyte concentrations. Three methods are investigated, including an algorithm named “sequential paired covariance” (SPC), which was recently reported. An improvement to this algorithm is also reported here. This new, improved method, named the “windowed mass selection method” (WMSM), is shown to effectively eliminate random noise that occurs in the data. This method is shown to be particularly useful in improving signal to noise ratios in both chromatographic and mass spectra for data acquired in peptide mapping of recombinant DNA derived proteins.