Kopriva, Ivica; Jerić, Ivanka; Brkljačić, Lidija (2015) Explicit–implicit mapping approach to nonlinear blind separation of sparse nonnegative dependent sources from a single mixture: pure component extraction from nonlinear mixture mass spectra. Journal of chemometrics, 29 (11). pp. 615626. ISSN 08869383

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Abstract
The nonlinear, nonnegative singlemixture blind source separation (BSS) problem consists of decomposing observed nonlinearly mixed multicomponent signal into nonnegative dependent component (source) signals. The problem is difficult and is a special case of the underdetermined BSS problem. However, it is practically relevant for the contemporary metabolic profiling of biological samples when only one sample is available for acquiring mass spectra ; afterwards, the pure components are extracted. Herein, we present a method for the blind separation of nonnegative dependent sources from a single, nonlinear mixture. First, an explicit feature map is used to map a single mixture into a pseudo multimixture. Second, an empirical kernel map is used for implicit mapping of a pseudo multimixture into a highdimensional reproducible kernel Hilbert space (RKHS). Under sparse probabilistic conditions that were previously imposed on sources, the singlemixture nonlinear problem is converted into an equivalent linear, multiplemixture problem that consists of the original sources and their higher order monomials. These monomials are suppressed by robust principal component analysis, hard, soft and trimmed thresholding. Sparseness constrained nonnegative matrix factorizations in RKHS yield sets of separated components. Afterwards, separated components are annotated with the pure components from the library using the maximal correlation criterion. The proposed method is depicted with a numerical example that is related to the extraction of 8 dependent components from 1 nonlinear mixture. The method is further demonstrated on 3 nonlinear chemical reactions of peptide synthesis in which 25, 19 and 28 dependent analytes are extracted from 1 nonlinear mixture mass spectra. The goal application of the proposed method is, in combination with other separation techniques, mass spectrometrybased nontargeted metabolic profiling, such as biomarker identification studies.
Item Type:  Article  

Uncontrolled Keywords:  Singlemixture nonlinear blind source separation; Explicit feature maps; Empirical kernel maps; Sparseness; Nonnegative matrix factorization; Mass spectrometry  
Subjects:  NATURAL SCIENCES > Mathematics > Applied Mathematics and Mathematical Modeling NATURAL SCIENCES > Chemistry TECHNICAL SCIENCES > Computing 

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:  13 Nov 2015 15:22  
URI:  http://fulir.irb.hr/id/eprint/2266  
DOI:  10.1002/cem.2745 
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