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Pregled bibliografske jedinice broj: 785747

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


Kopriva, Ivica; Jerić, Ivanka; Brkljačić, Lidija
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 (2015), 11; 615-626 doi:10.1002/cem.2745 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 785747 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
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

Autori
Kopriva, Ivica ; Jerić, Ivanka ; Brkljačić, Lidija

Izvornik
Journal of chemometrics (0886-9383) 29 (2015), 11; 615-626

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Single-mixture nonlinear blind source separation; Explicit feature maps; Empirical kernel maps; Sparseness; Nonnegative matrix factorization; Mass spectrometry.

Sažetak
The nonlinear, nonnegative single-mixture 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 multi-mixture. Second, an empirical kernel map is used for implicit mapping of a pseudo multi-mixture into a high-dimensional reproducible kernel Hilbert space (RKHS). Under sparse probabilistic conditions that were previously imposed on sources, the single-mixture nonlinear problem is converted into an equivalent linear, multiple-mixture 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 spectrometry-based non-targeted metabolic profiling, such as biomarker identification studies.

Izvorni jezik
Engleski

Znanstvena područja
Matematika, Kemija, Računarstvo



POVEZANOST RADA


Projekti:
HRZZ-9.01/232

Ustanove:
Institut "Ruđer Bošković", Zagreb

Profili:

Avatar Url Ivica Kopriva (autor)

Avatar Url Ivanka Jerić (autor)

Citiraj ovu publikaciju

Kopriva, Ivica; Jerić, Ivanka; Brkljačić, Lidija
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 (2015), 11; 615-626 doi:10.1002/cem.2745 (međunarodna recenzija, članak, znanstveni)
Kopriva, I., Jerić, I. & Brkljačić, L. (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), 615-626 doi:10.1002/cem.2745.
@article{article, year = {2015}, pages = {615-626}, DOI = {10.1002/cem.2745}, keywords = {Single-mixture nonlinear blind source separation, Explicit feature maps, Empirical kernel maps, Sparseness, Nonnegative matrix factorization, Mass spectrometry.}, journal = {Journal of chemometrics}, doi = {10.1002/cem.2745}, volume = {29}, number = {11}, issn = {0886-9383}, title = {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}, keyword = {Single-mixture nonlinear blind source separation, Explicit feature maps, Empirical kernel maps, Sparseness, Nonnegative matrix factorization, Mass spectrometry.} }
@article{article, year = {2015}, pages = {615-626}, DOI = {10.1002/cem.2745}, keywords = {Single-mixture nonlinear blind source separation, Explicit feature maps, Empirical kernel maps, Sparseness, Nonnegative matrix factorization, Mass spectrometry.}, journal = {Journal of chemometrics}, doi = {10.1002/cem.2745}, volume = {29}, number = {11}, issn = {0886-9383}, title = {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}, keyword = {Single-mixture nonlinear blind source separation, Explicit feature maps, Empirical kernel maps, Sparseness, Nonnegative matrix factorization, Mass spectrometry.} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


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