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    The organic chemistry of plant residues: Comparison of NMR and pyrolysis data using multivariate statistical approaches

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    Authors
    Plant, E.
    Smernik, R.
    Greenwood, Paul
    Macdonald, L.
    van Leeuwen, J.
    Date
    2013
    Type
    Journal Article
    
    Metadata
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    Citation
    Plant, E. and Smernik, R. and Greenwood, P. and Macdonald, L. and van Leeuwen, J. 2013. The organic chemistry of plant residues: Comparison of NMR and pyrolysis data using multivariate statistical approaches. Current Organic Chemistry. 17 (24): pp. 3006-3012.
    Source Title
    Current Organic Chemistry
    DOI
    10.2174/13852728113179990124
    ISSN
    1385-2728
    School
    Department of Chemistry
    URI
    http://hdl.handle.net/20.500.11937/58655
    Collection
    • Curtin Research Publications
    Abstract

    To effectively characterise and distinguish between different organic matter samples, multiple chemical characterisation techniques are often employed. Due to the structural complexity of organic matter and the unique information provided by different characterisation techniques, it is often difficult to compare and combine data obtained from different analytical methods. In this study, we show how non-parametric multivariate statistical approaches can be used to compare the relative pattern of similarity/dissimilarity between organic samples characterised by two common solid-state analytical techniques: 13C nuclear magnetic resonance (NMR) spectroscopy and flash pyrolysis-gas chromatography mass spectrometry (py-GCMS). These analytical methods were used to characterise a suite of plant residues including the leaf, flower, bark and wood of several species. Using non-parametric multivariate statistical approaches we identified similarities between the plant residue data using ordination plots, which enabled us to identify where NMR and py-GCMS distinguished between residues differently. A mantel-type test called RELATE showed that there was significant (P<0.05) similarity between the NMR and py-GCMS data in terms of their ability to differentiate between plant residues of different type; 61% of the sample discrimination was common to both profiling techniques, while 39% of discrimination was method specific. Further multivariate comparisons indicated that NMR was more sensitive to detecting differences in the organic composition of the plant residues. © 2013 Bentham Science Publishers.

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