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Wavelet-based method for noise characterization and rejection in high-performance liquid chromatography coupled to mass spectrometry

In English

Författare

Summary, in English

We present a new method for rejecting noise from HPLC-MS data sets. The algorithm reveals peptides at low concentrations by minimizing both the chemical and the random noise. The goal is reached through a systematic approach to characterize and remove the background. The data are represented as two-dimensional maps, in order to optimally exploit the complementary dimensions of separation of the peptides offered by the LC-MS technique. The virtual chromatograms, reconstructed from the spectrographic data, have proved to be more suitable to characterize the noise than the raw mass spectra. By means of wavelet analysis, it was possible to access both the chemical and the random noise, at different scales of the decomposition. The novel approach has proved to efficiently distinguish signal from noise and to selectively reject the background while preserving low-abundance peptides.

Publiceringsår

2008

Språk

Engelska

Sidor

4960-4968

Publikation/Tidskrift/Serie

Analytical Chemistry

Volym

80

Issue

13

Dokumenttyp

Artikel i tidskrift

Förlag

The American Chemical Society (ACS)

Ämne

  • Analytical Chemistry

Status

Published

ISBN/ISSN/Övrigt

  • ISSN: 1520-6882