prikaz prve stranice dokumenta Nonnegative Least Squares Approach to Quantification of 1H Nuclear Magnetic Resonance Spectra of Human Urine
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Scientific paper - Original scientific paper
Nonnegative Least Squares Approach to Quantification of 1H Nuclear Magnetic Resonance Spectra of Human Urine
Analytical Chemistry, 93 (2020), 2; 745-751. https://doi.org/10.1021/acs.analchem.0c02837
Kopriva, Ivica; Jerić, Ivanka; Popović Hadžija, Marijana; Hadžija, Mirko; Vučić Lovrenčić, Marijana

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Kopriva, I., Jerić, I., Popović Hadžija, M., Hadžija, M. & Vučić Lovrenčić, M. (2020). Nonnegative Least Squares Approach to Quantification of 1H Nuclear Magnetic Resonance Spectra of Human Urine. Analytical Chemistry, 93. (2), 745-751. doi: 10.1021/acs.analchem.0c02837

Kopriva, Ivica, et al. "Nonnegative Least Squares Approach to Quantification of 1H Nuclear Magnetic Resonance Spectra of Human Urine." Analytical Chemistry, vol. 93, no. 2, 2020, pp. 745-751. https://doi.org/10.1021/acs.analchem.0c02837

Kopriva, Ivica, Ivanka Jerić, Marijana Popović Hadžija, Mirko Hadžija and Marijana Vučić Lovrenčić. "Nonnegative Least Squares Approach to Quantification of 1H Nuclear Magnetic Resonance Spectra of Human Urine." Analytical Chemistry 93, no. 2 (2020): 745-751. https://doi.org/10.1021/acs.analchem.0c02837

Kopriva, I., et al. (2020) 'Nonnegative Least Squares Approach to Quantification of 1H Nuclear Magnetic Resonance Spectra of Human Urine', Analytical Chemistry, 93(2), pp. 745-751. doi: 10.1021/acs.analchem.0c02837

Kopriva I, Jerić I, Popović Hadžija M, Hadžija M, Vučić Lovrenčić M. Nonnegative Least Squares Approach to Quantification of 1H Nuclear Magnetic Resonance Spectra of Human Urine. Analytical Chemistry [Internet]. 2020 December 07 [cited 2024 November 26];93(2):745-751. doi: 10.1021/acs.analchem.0c02837

I. Kopriva, I. Jerić, M. Popović Hadžija, M. Hadžija and M. Vučić Lovrenčić, "Nonnegative Least Squares Approach to Quantification of 1H Nuclear Magnetic Resonance Spectra of Human Urine", Analytical Chemistry, vol. 93, no. 2, pp. 745-751, December 2020. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:264:969781. [Accessed: 26 November 2024]

Metadata
Title (english)Nonnegative Least Squares Approach to Quantification of 1H Nuclear Magnetic Resonance Spectra of Human Urine
AuthorIvica Kopriva
AuthorIvanka Jerić
AuthorMarijana Popović Hadžija
AuthorMirko Hadžija
AuthorMarijana Vučić Lovrenčić
Author's institutionRuđer Bošković Institute
Author's institution(Merkur University Hospital)
Scientific / art field,
discipline and subdiscipline		BIOMEDICINE AND HEALTHCARE
Clinical Medical Sciences
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Mathematics
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Chemistry
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract (english)
Due to its quantitative character and capability for high throughput screening, 1H nuclear magnetic resonance (NMR) spectroscopy is used extensively in the profiling of biofluids such as urine and blood plasma. However, the narrow frequency bandwidth of 1H NMR spectroscopy leads to a severe overlap of the spectra of components present in the complex mixtures such as biofluids. Therefore, 1H NMR-based metabolomics analysis is focused on targeted studies related to concentrations of the small number of metabolites. Here, we propose a library-based approach to quantify proportions of overlapping metabolites from 1H NMR mixture spectra. The method boils down to the linear nonnegative least squares (NNLS) problem, whereas proportions of the pure components contained in the library stand for the unknowns. The method is validated on an estimation of the proportions of: (i) the 78 pure spectra, presumably related to type 2 diabetes mellitus (T2DM), from their synthetic linear mixture ; (ii) metabolites present in 62 1H NMR spectra of urine of subjects with T2DM and 62 1H NMR spectra of urine of control subjects. In both cases, the in-house library of 210 pure components 1H NMR spectra represented the design matrix in related NNLS problem. The proposed method pinpoints 63 metabolites that in a statistically significant way discriminate T2DM group from the control group, and 46 metabolites discriminating control from the T2DM group. For several T2DM- discriminative metabolites, we prove their presence by an independent analytical determination or by pointing out the corresponding findings in the published literature.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Journal titleAnalytical Chemistry
Numberingvol. 93, no. 2, pp. 745-751
p-ISSN0003-2700
e-ISSN1520-6882
DOIhttps://doi.org/10.1021/acs.analchem.0c02837
URN:NBNurn:nbn:hr:264:969781
Online first2020-12-07
ProjectNumber: IP-2016-06-5235
Title (croatian): Strukturne dekompozicije empirijskih podataka za računalno potpomognutu dijagnostiku bolesti
Acronym: DEDAD
Leader: Ivica Kopriva
Jurisdiction: Croatia
Funding stream: 594.400,00 HRK
Document URLhttps://pubs.acs.org/doi/10.1021/acs.analchem.0c02837
Type of resourceText
Access conditionsOpen access
Terms of useLicence Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) icon
Created on2021-07-07 09:16:26