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Multivariate Analysis of 2D-NMR Spectroscopy: Applications in wood science and metabolomics
Umeå University, Faculty of Science and Technology, Department of Chemistry. (Mattias Hedenström)
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Wood is our most important renewable resource. We need better quality and quantity both according to the wood itself and the processes that are using wood as a raw material. Hence, the understanding of the chemical composition of the wood is of high importance. Improved and new methods for analyzing wood are important to achieve better knowledge about both refining processes and raw material. The combination of NMR and multivariate analyses (MVA) is a powerful method for these analyses but so far it has been limited mainly to 1D NMR. In this project, we have developed methods for combining 2D NMR and MVA in both wood analysis and metabolomics. This combination was used to compare samples from normal wood and tension wood, and also trees with a down regulation of a pectin responsible gene. Dissolving pulp was also examined using the same combination of 2D-NMR and MVA, together with FT-IR and solid state 13C CP-MAS NMR. Here we focused on the difference between wood type (softwood and hardwood), process type (sulfite and sulfate) and viscosity. These methods confirmed and added knowledge about the dissolving pulp. Also reactivity was compared in relation to morphology of the cellulose and pulp composition. Based on the method and software used in the wood analysis projects, a new method called HSQC-STOCSY was developed. This method is especially suited for assignment of substances in complex mixtures. Peaks in 2D NMR spectra that correlate between different samples are plotted in correlation plots resembling regular NMR spectra. These correlation plots have great potential in identifying individual components in complex mixtures as shown here in a metabolic data set. This method could potentially also be used in other areas such as drug/target analyses, protein dynamics and assignment of wood spectra.

Place, publisher, year, edition, pages
Umeå: Umeå Universitet , 2013. , 56 p.
Keyword [en]
2D NMR, HSQC, cellulose, ligning, STOCSY, HSQC-STOCSY, crystallinity
National Category
Chemical Sciences
Research subject
biological chemistry
Identifiers
URN: urn:nbn:se:umu:diva-80201ISBN: 978-91-7459-728-8 (print)OAI: oai:DiVA.org:umu-80201DiVA: diva2:647421
Public defence
2013-10-04, KBC-huset, KB3B1, Umeå universitet, Umeå, 13:00 (Swedish)
Opponent
Supervisors
Available from: 2013-09-13 Created: 2013-09-11 Last updated: 2013-09-11Bibliographically approved
List of papers
1. Identification of lignin and polysaccharide modifications in Populus wood by chemometric analysis of 2D NMR spectra from dissolved cell walls
Open this publication in new window or tab >>Identification of lignin and polysaccharide modifications in Populus wood by chemometric analysis of 2D NMR spectra from dissolved cell walls
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2009 (English)In: Molecular Plant, ISSN 1674-2052, Vol. 2, no 5, 933-942 p.Article in journal (Refereed) Published
Abstract [en]

2D (13)C-(1)H HSQC NMR spectroscopy of acetylated cell walls in solution gives a detailed fingerprint that can be used to assess the chemical composition of the complete wall without extensive degradation. We demonstrate how multivariate analysis of such spectra can be used to visualize cell wall changes between sample types as high-resolution 2D NMR loading spectra. Changes in composition and structure for both lignin and polysaccharides can subsequently be interpreted on a molecular level. The multivariate approach alleviates problems associated with peak picking of overlapping peaks, and it allows the deduction of the relative importance of each peak for sample discrimination. As a first proof of concept, we compare Populus tension wood to normal wood. All well established differences in cellulose, hemicellulose, and lignin compositions between these wood types were readily detected, confirming the reliability of the multivariate approach. In a second example, wood from transgenic Populus modified in their degree of pectin methylesterification was compared to that of wild-type trees. We show that differences in both lignin and polysaccharide composition that are difficult to detect with traditional spectral analysis and that could not be a priori predicted were revealed by the multivariate approach. 2D NMR of dissolved cell wall samples combined with multivariate analysis constitutes a novel approach in cell wall analysis and provides a new tool that will benefit cell wall research.

Keyword
Aspen, biostatistics, cell walls, multivariate data analysis, NMR spectroscopy, tension wood
National Category
Chemical Sciences
Identifiers
urn:nbn:se:umu:diva-26780 (URN)10.1093/mp/ssp047 (DOI)19825670 (PubMedID)
Available from: 2009-10-27 Created: 2009-10-27 Last updated: 2013-09-11Bibliographically approved
2. Characterization of dissolving pulp by multivariate data analysis of FT-IR and NMR spectra
Open this publication in new window or tab >>Characterization of dissolving pulp by multivariate data analysis of FT-IR and NMR spectra
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2011 (English)In: Nordic Pulp & Paper Research Journal, ISSN 0283-2631, E-ISSN 2000-0669, Vol. 26, no 4, 398-409 p.Article in journal (Refereed) Published
Abstract [en]

Several grades of dissolving pulps have been analyzed using FT-IR, solid state13C NMR and two dimensional1H-13C HSQC NMR spectroscopy to obtain an extensive data set for further characterization. The selection of the dissolving pulps with high cellulose purity was based on pulping process, wood type and, intrinsic pulp viscosity. Multivariate data analysis was used to investigate how information derived from the spectroscopic data correlate to each of the selection criterion: wood type, process type and viscosity. The spectroscopic methods were also compared with common dissolving pulp analyses to see to what extent spectroscopy can predict pulp analyses.

Correlations were found between the spectroscopic data and the pulp characteristics process type and wood type, but not for intrinsic viscosity. A reason for a good correlation to wood type appears to be the hemicelluloses composition, expressed as the xylose:mannose ratio by 2D NMR spectroscopy. For process type, 2D NMR showed the most characteristic property to be the amount of reducing ends in the cellulosic samples, which in turn strongly correlates to lower molecular weight for the sulfite samples as determined by molecular weight distribution.

Many common, yet expensive and time consuming, pulp analyses could also be predicted by the achieved models. It can be concluded that investigations of dissolving pulp characteristics, especially concerning different wood and process types, can take advantage of the methods and models presented in this study.

Place, publisher, year, edition, pages
Stockholm: AB Svensk papperstidning, 2011
Keyword
Cellulose, Dissolving pulp, Hemicelluloses, FT-IR spectroscopy, Multivariate data analysis, NMR spectroscopy, Pulp viscosity, Wood type
National Category
Chemical Sciences Paper, Pulp and Fiber Technology
Identifiers
urn:nbn:se:umu:diva-51978 (URN)000298868000006 ()
Available from: 2012-02-06 Created: 2012-02-06 Last updated: 2017-12-08Bibliographically approved
3. Reactivity of dissolving pulp analyzed with multivariate data analysis of XRD and NMR data.
Open this publication in new window or tab >>Reactivity of dissolving pulp analyzed with multivariate data analysis of XRD and NMR data.
(English)Manuscript (preprint) (Other academic)
National Category
Natural Sciences
Identifiers
urn:nbn:se:umu:diva-80184 (URN)
Available from: 2013-09-11 Created: 2013-09-11 Last updated: 2013-09-11Bibliographically approved
4. Identification of metabolites from 2D 1H-13C HSQC NMR using peak correlation plots
Open this publication in new window or tab >>Identification of metabolites from 2D 1H-13C HSQC NMR using peak correlation plots
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2014 (English)In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 15, 413Article in journal (Refereed) Published
Abstract [en]

Background: Identification of individual components in complex mixtures is an important and sometimes daunting task in several research areas like metabolomics and natural product studies. NMR spectroscopy is an excellent technique for analysis of mixtures of organic compounds and gives a detailed chemical fingerprint of most individual components above the detection limit. For the identification of individual metabolites in metabolomics, correlation or covariance between peaks in 1H NMR spectra has previously been successfully employed. Similar correlation of 2D 1H-13C Heteronuclear Single Quantum Correlation spectra was recently applied to investigate the structure of heparine. In this paper, we demonstrate how a similar approach can be used to identify metabolites in human biofluids (post-prostatic palpation urine).

Results: From 50 1H-13C Heteronuclear Single Quantum Correlation spectra, 23 correlation plots resembling pure metabolites were constructed. The identities of these metabolites were confirmed by comparing the correlation plots with reported NMR data, mostly from the Human Metabolome Database.

Conclusions: Correlation plots prepared by statistically correlating 1H-13C Heteronuclear Single Quantum Correlation spectra from human biofluids provide unambiguous identification of metabolites. The correlation plots highlight cross-peaks belonging to each individual compound, not limited by long-range magnetization transfer as conventional NMR experiments.

Keyword
HSQC, Correlation, Metabolite, Biofluid, Identification
National Category
Chemical Sciences
Identifiers
urn:nbn:se:umu:diva-80196 (URN)10.1186/s12859-014-0413-z (DOI)000347650900001 ()
Note

Originally published in manuscript form.

Available from: 2013-09-11 Created: 2013-09-11 Last updated: 2017-12-06Bibliographically approved

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