Abstract

Multivariate analysis on spectroscopic H-1 NMR data is well established in metabolomics and other fields where the composition of complex samples is studied. However, biornarker identification can be hampered by overlapping resonances. 2D NMR data provides a more detailed "fingerprint" of the chemical structure and composition of the sample with greatly improved spectral resolution compared to H-1 NMR data. In this report, we demonstrate a procedure for the construction of multivariate models based on frequency domain 2D NMR data where the loadings can be visualized as highly informative 2D loading spectra. This method is based on the analysis of raw spectral data without any need for peak picking or integration prior to analysis. Spectral features such as line widths and peak positions are thus retained. Hence, the loadings can be visualized and interpreted on a molecular level as pseudo 2D spectra in order to identify potential biomarkers. To demonstrate this strategy we have analyzed HSQC spectra acquired from populus phloem plant extracts originating from a set of designed experiments with OPLS regression. (C) 2008 Elsevier B.V. All rights reserved.

Keywords

metabolomics; two-dimensional NMR spectroscopy; HSQC; multivariate data analysis; OPLS; S-plot

Published in

Chemometrics and Intelligent Laboratory Systems
2008, Volume: 92, number: 2, pages: 110-117
Publisher: ELSEVIER SCIENCE BV

SLU Authors

• Sundberg, Björn

  • Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Analytical Chemistry


Publication identifier

DOI: https://doi.org/10.1016/j.chemolab.2008.01.003

Permanent link to this page (URI)

https://res.slu.se/id/publ/78133