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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 (2019) Library-Assisted Nonlinear Blind Separation and Annotation of Pure Components from a Single 1H Nuclear Magnetic Resonance Mixture Spectra. Analytica Chimica Acta, 1080 . pp. 55-65. ISSN 0003-2670

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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.

Item Type: Article
Additional Information: The work performed has been supported through grant IP-2016-06-5235 "Structured decompositions of empirical data for computationally-assisted diagnosis of disease" funded by the Croatian Science Foundation. The first author thanks Gary McGuire for language editing of the final version of the manuscript.
Uncontrolled Keywords: nonlinear blind source separation ; single mixture ; multiple reproducible kernel Hilbert spaces ; nonnegative sparse matrix factorization ; 1H NMR spectroscopy ; metabolic profiling
Subjects: NATURAL SCIENCES > Mathematics
Divisions: Division of Electronics
Division of Molecular Medicine
Division of Organic Chemistry and Biochemistry
Project titleProject leaderProject codeProject type
Strukturne dekompozicije empirijskih podataka za računalno potpomognutu dijagnostiku bolesti-DEDADIvica KoprivaIP-2016-06-5235HRZZ
Depositing User: Ivanka Jerić
Date Deposited: 19 Nov 2019 12:27
DOI: 10.1016/j.aca.2019.07.004

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