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Chemometric Analysis of NMR Spectra

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Modern Magnetic Resonance

Abstract

NMR is one of the most powerful analytical techniques of our time. It allows detailed investigation of qualitative and quantitative characteristics of complex chemical and biological samples. The resulting NMR data provides a wealth of information about the samples, but the NMR data analysis has been and still is suffering from oversimplified approaches making it difficult to extract all the information efficiently. For instance, univariate methods that just use one or a few selected variables for the analysis from a whole spectrum lead to a huge loss of information. Such a simplifying approach reduces the chance of discovering new findings and truly learning about complex aspects of the samples investigated. Multivariate data analysis techniques allows for truly exploratory and comprehensive analysis of NMR data. This is particularly advantageous in the investigation of complex biological samples. Chemometrics can be helpful here by providing tools for unsupervised and supervised data exploration, multivariate calibration, classification and discrimination.

This chapter presents some important steps in the pre-processing of NMR data as well as some of the most common chemometric techniques for data exploration and analysis. An example of NMR spectra of apple juice samples is given, to illustrate the power of the combination of NMR data and chemometrics.

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Authors and Affiliations

  1. Department of Food Science, University of Copenhagen, Rolighedsvej 26, DK-1958, Frederiksberg C, Denmark

    Parvaneh Ebrahimi, Nanna Viereck, Rasmus Bro & Søren B. Engelsen

Authors
  1. Parvaneh Ebrahimi
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  2. Nanna Viereck
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  3. Rasmus Bro
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  4. Søren B. Engelsen
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Corresponding author

Correspondence to Søren B. Engelsen .

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Editors and Affiliations

  1. Royal Society of Chemistry, London, United Kingdom

    Graham A. Webb

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Ebrahimi, P., Viereck, N., Bro, R., Engelsen, S.B. (2017). Chemometric Analysis of NMR Spectra. In: Webb, G. (eds) Modern Magnetic Resonance. Springer, Cham. https://doi.org/10.1007/978-3-319-28275-6_20-1

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  • DOI: https://doi.org/10.1007/978-3-319-28275-6_20-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28275-6

  • Online ISBN: 978-3-319-28275-6

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