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Blind Separation of Analytes in Nuclear Magnetic Resonance Spectroscopy: Improved Model for Nonnegative Matrix Factorization

Kopriva, Ivica; Jerić, Ivanka (2014) Blind Separation of Analytes in Nuclear Magnetic Resonance Spectroscopy: Improved Model for Nonnegative Matrix Factorization. Chemometrics and Intelligent Laboratory Systems, 137 . pp. 47-56. ISSN 0169-7439

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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.

Item Type: Article
Additional Information: NOTICE: this is the author’s version of a work that was accepted for publication in Chemometrics and Intelligent Laboratory Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Chemometrics and Intelligent Laboratory Systems, vol. 137 (2014), DOI: 10.1016/j.chemolab.2014.06.004
Uncontrolled Keywords: Nuclear magnetic resonance spectroscopy; (non-)linear mixture model, blind source separation; nonnegative matrix factorization; compound identification
Subjects: TECHNICAL SCIENCES > Computing
Divisions: Division of Laser and Atomic Research and Development
Division of Organic Chemistry and Biochemistry
Project titleProject leaderProject codeProject type
Nonlinear component analysis with applications in chemometrics and pathologyIvica Kopriva09.01/232MZOS
Depositing User: Ivica Kopriva
Date Deposited: 28 Apr 2015 14:07
DOI: 10.1016/j.chemolab.2014.06.004

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