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Pregled bibliografske jedinice broj: 703050

Blind separation of analytes in nuclear magnetic resonance spectroscopy: improved model for nonnegative matrix factorization


Kopriva, Ivica; Jerić, Ivanka
Blind separation of analytes in nuclear magnetic resonance spectroscopy: improved model for nonnegative matrix factorization // Chemometrics and intelligent laboratory systems, 137 (2014), 47-56 doi:10.1016/j.chemolab.2014.06.004 (međunarodna recenzija, članak, znanstveni)


Naslov
Blind separation of analytes in nuclear magnetic resonance spectroscopy: improved model for nonnegative matrix factorization

Autori
Kopriva, Ivica ; Jerić, Ivanka

Izvornik
Chemometrics and intelligent laboratory systems (0169-7439) 137 (2014); 47-56

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

Ključne riječi
Nuclear magnetic resonance spectroscopy; (non-)linear mixture model; blind source separation; nonnegative matrix factorization; compound identification

Sažetak
We introduce an improved model for sparseness-constrained nonnegative matrix factorization (sNMF) of amplitude nuclear magnetic resonance (NMR) spectra of mixtures into a greater number of component spectra. In the proposed method, the selected sNMF algorithm is applied to the square of the amplitude of the NMR spectrum of the mixture instead of to the amplitude spectrum itself. Afterwards, the square roots of separated squares of the component spectra and the concentration matrix yield estimates of the true component amplitude spectrum and of the concentration matrix. The proposed model remains linear on average when the number of overlapping components is increasing, while the model based on the amplitude spectra of the mixtures deviates from the linear one when the number of overlapping components is increased. This is demonstrated through the conducted sensitivity analysis. Thus, the proposed model improves the capability of the sparse NMF algorithms to separate correlated (overlapping) component spectra from the smaller number of mixture NMR spectra. This is demonstrated in two experimental scenarios: extraction of three correlated component spectra from two 1H NMR mixture spectra and extraction of four correlated component spectra from three COSY NMR mixture spectra. The proposed method can increase efficiency in a spectral library search by reducing the occurrence of false positives and false negatives. That, in turn, can yield better accuracy in biomarker identification studies, which makes the proposed method important for natural product research and the field of metabolic studies.

Izvorni jezik
Engleski

Znanstvena područja
Matematika, Kemija, Računarstvo



POVEZANOST RADA


Projekt / tema
HRZZ-09.01/232

Ustanove
Institut "Ruđer Bošković", Zagreb

Citiraj ovu publikaciju

Kopriva, Ivica; Jerić, Ivanka
Blind separation of analytes in nuclear magnetic resonance spectroscopy: improved model for nonnegative matrix factorization // Chemometrics and intelligent laboratory systems, 137 (2014), 47-56 doi:10.1016/j.chemolab.2014.06.004 (međunarodna recenzija, članak, znanstveni)
Kopriva, I. & Jerić, I. (2014) Blind separation of analytes in nuclear magnetic resonance spectroscopy: improved model for nonnegative matrix factorization. Chemometrics and intelligent laboratory systems, 137, 47-56 doi:10.1016/j.chemolab.2014.06.004.
@article{article, year = {2014}, pages = {47-56}, DOI = {10.1016/j.chemolab.2014.06.004}, keywords = {nuclear magnetic resonance spectroscopy, (non-)linear mixture model, blind source separation, nonnegative matrix factorization, compound identification}, journal = {Chemometrics and intelligent laboratory systems}, doi = {10.1016/j.chemolab.2014.06.004}, volume = {137}, issn = {0169-7439}, title = {Blind separation of analytes in nuclear magnetic resonance spectroscopy: improved model for nonnegative matrix factorization}, keyword = {nuclear magnetic resonance spectroscopy, (non-)linear mixture model, blind source separation, nonnegative matrix factorization, compound identification} }

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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