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Pregled bibliografske jedinice broj: 1015210

Library-Assisted Nonlinear Blind Separation and Annotation of Pure Components from a Single 1H Nuclear Magnetic Resonance Mixture Spectra


Kopriva, Ivica; Jerić, Ivanka; Popović Hadžija, Marijana; Hadžija, Mirko; Vučić Lovrenčić, Marijana; Brkljačić, Lidija
Library-Assisted Nonlinear Blind Separation and Annotation of Pure Components from a Single 1H Nuclear Magnetic Resonance Mixture Spectra // Analytica chimica acta, 1080 (2019), 55-65 doi:10.1016/j.aca.2019.07.004 (međunarodna recenzija, članak, znanstveni)


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Naslov
Library-Assisted Nonlinear Blind Separation and Annotation of Pure Components from a Single 1H Nuclear Magnetic Resonance Mixture Spectra

Autori
Kopriva, Ivica ; Jerić, Ivanka ; Popović Hadžija, Marijana ; Hadžija, Mirko ; Vučić Lovrenčić, Marijana ; Brkljačić, Lidija

Izvornik
Analytica chimica acta (0003-2670) 1080 (2019); 55-65

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

Ključne riječi
nonlinear blind source separation ; single mixture ; multiple reproducible kernel Hilbert spaces ; nonnegative sparse matrix factorization ; 1H NMR spectroscopy ; metabolic profiling

Sažetak
Due to its capability for high-throughput screening 1H nuclear magnetic resonance (NMR) spectroscopy is commonly used for metabolite research. The key problem in 1H NMR spectroscopy of multicomponent mixtures is overlapping of component signals and that is increasing with the number of components, their complexity and structural similarity. It makes metabolic profiling, that is carried out through matching acquired spectra with metabolites from the library, a hard problem. Here, we propose a method for nonlinear blind separation of highly correlated components spectra from a single 1H NMR mixture spectra. The method transforms a single nonlinear mixture into multiple high-dimensional reproducible kernel Hilbert Spaces (mRKHSs). Therein, highly correlated components are separated by sparseness constrained nonnegative matrix factorization in each induced RKHS. Afterwards, metabolites are identified through comparison of separated components with the library comprised of 160 pure components. Thereby, a significant number of them are expected to be related with diabetes type 2. Conceptually similar methodology for nonlinear blind separation of correlated components from two or more mixtures is presented in the Supplementary material. Single-mixture blind source separation is exemplified on: (i) annotation of five components spectra separated from one 1H NMR model mixture spectra ; (ii) annotation of fifty five metabolites separated from one 1H NMR mixture spectra of urine of subjects with and without diabetes type 2. Arguably, it is for the first time a method for blind separation of a large number of components from a single nonlinear mixture has been proposed. Moreover, the proposed method pinpoints urinary creatine, glutamic acid and 5-hydroxyindoleacetic acid as the most prominent metabolites in samples from subjects with diabetes type 2, when compared to healthy controls.

Izvorni jezik
Engleski

Znanstvena područja
Matematika, Kemija, Računarstvo, Temeljne medicinske znanosti



POVEZANOST RADA


Projekti:
HRZZ-IP-2016-06-5235 - Strukturne dekompozicije empirijskih podataka za računalno potpomognutu dijagnostiku bolesti (DEDAD) (Kopriva, Ivica, HRZZ - 2016-06) ( POIROT)

Ustanove:
Klinička bolnica "Merkur",
Klinika za dijabetes, endokrinologiju i bolesti metabolizma Vuk Vrhovac,
Institut "Ruđer Bošković", Zagreb

Citiraj ovu publikaciju

Kopriva, Ivica; Jerić, Ivanka; Popović Hadžija, Marijana; Hadžija, Mirko; Vučić Lovrenčić, Marijana; Brkljačić, Lidija
Library-Assisted Nonlinear Blind Separation and Annotation of Pure Components from a Single 1H Nuclear Magnetic Resonance Mixture Spectra // Analytica chimica acta, 1080 (2019), 55-65 doi:10.1016/j.aca.2019.07.004 (međunarodna recenzija, članak, znanstveni)
Kopriva, I., Jerić, I., Popović Hadžija, M., Hadžija, M., Vučić Lovrenčić, M. & Brkljačić, L. (2019) Library-Assisted Nonlinear Blind Separation and Annotation of Pure Components from a Single 1H Nuclear Magnetic Resonance Mixture Spectra. Analytica chimica acta, 1080, 55-65 doi:10.1016/j.aca.2019.07.004.
@article{article, year = {2019}, pages = {55-65}, DOI = {10.1016/j.aca.2019.07.004}, keywords = {nonlinear blind source separation, single mixture, multiple reproducible kernel Hilbert spaces, nonnegative sparse matrix factorization, 1H NMR spectroscopy, metabolic profiling}, journal = {Analytica chimica acta}, doi = {10.1016/j.aca.2019.07.004}, volume = {1080}, issn = {0003-2670}, title = {Library-Assisted Nonlinear Blind Separation and Annotation of Pure Components from a Single 1H Nuclear Magnetic Resonance Mixture Spectra}, keyword = {nonlinear blind source separation, single mixture, multiple reproducible kernel Hilbert spaces, nonnegative sparse matrix factorization, 1H NMR spectroscopy, metabolic profiling} }
@article{article, year = {2019}, pages = {55-65}, DOI = {10.1016/j.aca.2019.07.004}, keywords = {nonlinear blind source separation, single mixture, multiple reproducible kernel Hilbert spaces, nonnegative sparse matrix factorization, 1H NMR spectroscopy, metabolic profiling}, journal = {Analytica chimica acta}, doi = {10.1016/j.aca.2019.07.004}, volume = {1080}, issn = {0003-2670}, title = {Library-Assisted Nonlinear Blind Separation and Annotation of Pure Components from a Single 1H Nuclear Magnetic Resonance Mixture Spectra}, keyword = {nonlinear blind source separation, single mixture, multiple reproducible kernel Hilbert spaces, nonnegative sparse matrix factorization, 1H NMR spectroscopy, metabolic profiling} }

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
  • MEDLINE


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