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Multi‐component analysis: blind extraction of pure components mass spectra using sparse component analysis
Author(s) -
Kopriva Ivica,
Jerić Ivanka
Publication year - 2009
Publication title -
journal of mass spectrometry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.475
H-Index - 121
eISSN - 1096-9888
pISSN - 1076-5174
DOI - 10.1002/jms.1627
Subject(s) - mass spectrum , principal component analysis , blind signal separation , chemistry , component analysis , a priori and a posteriori , component (thermodynamics) , spectral line , non negative matrix factorization , matrix (chemical analysis) , independent component analysis , biological system , matrix decomposition , pattern recognition (psychology) , mass spectrometry , computer science , artificial intelligence , chromatography , thermodynamics , eigenvalues and eigenvectors , computer network , channel (broadcasting) , philosophy , astronomy , biology , physics , epistemology , quantum mechanics
The paper presents sparse component analysis (SCA)‐based blind decomposition of the mixtures of mass spectra into pure components, wherein the number of mixtures is less than number of pure components. Standard solutions of the related blind source separation (BSS) problem that are published in the open literature require the number of mixtures to be greater than or equal to the unknown number of pure components. Specifically, we have demonstrated experimentally the capability of the SCA to blindly extract five pure components mass spectra from two mixtures only. Two approaches to SCA are tested: the first one based on ℓ 1 norm minimization implemented through linear programming and the second one implemented through multilayer hierarchical alternating least square nonnegative matrix factorization with sparseness constraints imposed on pure components spectra. In contrast to many existing blind decomposition methods no a priori information about the number of pure components is required. It is estimated from the mixtures using robust data clustering algorithm together with pure components concentration matrix. Proposed methodology can be implemented as a part of software packages used for the analysis of mass spectra and identification of chemical compounds. Copyright © 2009 John Wiley & Sons, Ltd.