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

Journal Article · · BioEnergy Research
 [1];  [2];  [3];  [4];  [4];  [5];  [6];  [7];  [2]
  1. Univ. of Queensland, Queensland (Australia); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  2. Univ. of Queensland, Queensland (Australia)
  3. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Sandia National Lab. (SNL-CA), Livermore, CA (United States)
  4. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); National Renewable Energy Lab. (NREL), Golden, CO (United States)
  5. Univ. of the Sunshine Coast and Queensland Dept. of Agriculture, Fisheries and Forestry, Queensland (Australia)
  6. Southern Cross Univ., East Lismore (Australia)
  7. Univ. of Queensland, Queensland (Australia); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Sandia National Lab. (SNL-CA), Livermore, CA (United States)
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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. In conclusion, 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.

Research Organization:
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Sponsoring Organization:
USDOE Office of Science (SC)
Grant/Contract Number:
AC36-08GO28308
OSTI ID:
1225019
Report Number(s):
NREL/JA-5100-65143
Journal Information:
BioEnergy Research, Vol. 8, Issue 3; Related Information: BioEnergy Research; ISSN 1939-1234
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 11 works
Citation information provided by
Web of Science

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Cited By (3)

Infrared and Raman spectra of lignin substructures: Coniferyl alcohol, abietin, and coniferyl aldehyde journal April 2019
Infrared and Raman spectra of lignin substructures: Dibenzodioxocin journal November 2019
Evaluating Lignocellulosic Biomass, Its Derivatives, and Downstream Products with Raman Spectroscopy journal April 2015

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Related Subjects

09 BIOMASS FUELS
59 BASIC BIOLOGICAL SCIENCES
lignocellulose
Raman spectroscopy
high-throughput
multivariate analysis
lignin S/G
eucalyptus
Corymbia
Acacia