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.
Keywords: Multivariate statistics, NMR, Organic matter, Plant residue, Pyrolysis, RELATE, Resemblance.
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