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High-Throughput Prediction of Acacia and Eucalypt Lignin Syringyl/Guaiacyl Content Using FT-Raman Spectroscopy and Partial Least Squares Modeling

Lupoi, Jason S. and Healey, Adam and Singh, Seema and Sykes, Robert and Davis, Mark and Lee, David J. and Shepherd, Merv and 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 Modeling. BioEnergy Research . ISSN 1939-1234

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Article Link(s): http://dx.doi.org/10.1007/s12155-015-9578-1

Publisher URL: http://link.springer.com/article/10.1007%2Fs12155-015-9578-1

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.

Item Type:Article
Business groups:Horticulture and Forestry Science
Keywords:Acacia Corymbia Eucalyptus High-throughput Lignin S/G Lignocellulose Multivariate analysis Raman spectroscopy Biomass Feedstocks Forecasting Least squares approximations Lignin Mass spectrometry Multivariant analysis High throughput Multi variate analysis Throughput
Subjects:Technology > Technology (General) > Spectroscopy
Forestry > Research. Experimentation
Deposited On:18 Mar 2015 02:06
Last Modified:18 Mar 2015 02:06

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