Kopriva, Ivica; Jerić, Ivanka (2009) Multicomponent Analysis: Blind Extraction of Pure Components Mass Spectra using Sparse Component Analysis. Journal of Mass Spectrometry, 44 (9). pp. 13781388. ISSN 10765174

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Abstract
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 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.
Item Type:  Article  

Additional Information:  This is the peer reviewed version of the following article: Multicomponent Analysis: Blind Extraction of Pure Components Mass Spectra using Sparse Component Analysis, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/jms.1627/full. This article may be used for noncommercial purposes in accordance with Wiley Terms and Conditions for SelfArchiving.  
Uncontrolled Keywords:  mass spectrometry; chemometrics; blind source separation; sparse component analysis; nonnegative matrix factorization  
Subjects:  NATURAL SCIENCES > Mathematics > Applied Mathematics and Mathematical Modeling NATURAL SCIENCES > Chemistry > Analytic Chemistry TECHNICAL SCIENCES > Computing > Data Processing 

Divisions:  Division of Laser and Atomic Research and Development Division of Organic Chemistry and Biochemistry 

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Depositing User:  Ivica Kopriva  
Date Deposited:  04 Dec 2015 08:42  
URI:  http://fulir.irb.hr/id/eprint/2379  
DOI:  10.10002/jms.1627 
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