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Article

Wavelet-Based Method for Noise Characterization and Rejection in High-Performance Liquid Chromatography Coupled to Mass Spectrometry

Salvatore Cappadona*^†^‡, Fredrik Levander^‡, Maria Jansson^‡, Peter James^‡, Sergio Cerutti^† and Linda Pattini^†

Department of Bioengineering, IIT Unit, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy, and Department of Protein Technology, Lund University, BMC D13, SE-22184 Lund, Sweden

Anal. Chem., 2008, 80 (13), pp 4960–4968

DOI: 10.1021/ac800166w

Publication Date (Web): May 30, 2008

* To whom correspondence should be addressed. E-mail: salvatore.cappadona@biomed.polimi.it. Phone: +39 02 2399 3322 . Fax: +39 02 2399 3360.

†

Politecnico di Milano.

‡

Lund University.

Abstract

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.

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Published In Issue July 01, 2008
Article ASAPMay 30, 2008
Received: January 23, 2008
Accepted: April 4, 2008

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Article

Wavelet-Based Method for Noise Characterization and Rejection in High-Performance Liquid Chromatography Coupled to Mass Spectrometry

Abstract

Tools

SciFinder Links

History

Recommend & Share

Related Content

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Application of Linear and Nonlinear Discrete Wavelet Transforms to MALDI-MS Measurements of Bacteria for Classification

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