High-throughput prediction of Acacia and eucalypt lignin syringyl/guaiacyl content using FT-Raman spectroscopy and partial least squares modelling

Lupoi, Jason S., Healey, Adam, Singh, Seema, Sykes, Robert, Davis, Mark, Lee, David J., Shepherd, Merv, Simmons, Blake A. and Henry, Robert J. (2015) High-throughput prediction of Acacia and eucalypt lignin syringyl/guaiacyl content using FT-Raman spectroscopy and partial least squares modelling. Bioenergy Research, 8 3: 953-963. doi:10.1007/s12155-015-9578-1


Author Lupoi, Jason S.
Healey, Adam
Singh, Seema
Sykes, Robert
Davis, Mark
Lee, David J.
Shepherd, Merv
Simmons, Blake A.
Henry, Robert J.
Title High-throughput prediction of Acacia and eucalypt lignin syringyl/guaiacyl content using FT-Raman spectroscopy and partial least squares modelling
Journal name Bioenergy Research   Check publisher's open access policy
ISSN 1939-1234
1939-1242
Publication date 2015-01-16
Year available 2015
Sub-type Article (original research)
DOI 10.1007/s12155-015-9578-1
Open Access Status
Volume 8
Issue 3
Start page 953
End page 963
Total pages 11
Place of publication New York, NY United States
Publisher Springer New York LLC
Collection year 2015
Language eng
Formatted abstract
High-throughput techniques are necessary to efficiently screen potential lignocellulosic feedstocks for the production of renewable fuels, chemicals, and bio-based materials, thereby reducing experimental time and expense while supplanting tedious, destructive methods. The ratio of lignin syringyl (S) to guaiacyl (G) monomers has been routinely quantified as a way to probe biomass recalcitrance. Mid-infrared and Raman spectroscopy have been demonstrated to produce robust partial least squares models for the prediction of lignin S/G ratios in a diverse group of Acacia and eucalypt trees. The most accurate Raman model has now been used to predict the S/G ratio from 269 unknown Acacia and eucalypt feedstocks. This study demonstrates the application of a partial least squares model composed of Raman spectral data and lignin S/G ratios measured using pyrolysis/molecular beam mass spectrometry (pyMBMS) for the prediction of S/G ratios in an unknown data set. The predicted S/G ratios calculated by the model were averaged according to plant species, and the means were not found to differ from the pyMBMS ratios when evaluating the mean values of each method within the 95 % confidence interval. Pairwise comparisons within each data set were employed to assess statistical differences between each biomass species. While some pairwise appraisals failed to differentiate between species, Acacias, in both data sets, clearly display significant differences in their S/G composition which distinguish them from eucalypts. This research shows the power of using Raman spectroscopy to supplant tedious, destructive methods for the evaluation of the lignin S/G ratio of diverse plant biomass materials.
Keyword Lignocellulose
Raman spectroscopy
High throughput
Multivariate analysis
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Queensland Alliance for Agriculture and Food Innovation
Official 2016 Collection
 
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Citation counts: TR Web of Science Citation Count  Cited 2 times in Thomson Reuters Web of Science Article | Citations
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Created: Thu, 29 Jan 2015, 11:23:06 EST by Professor Robert Henry on behalf of Qld Alliance for Agriculture and Food Innovation