High-Throughput Prediction of Acacia and Eucalypt Lignin Syringyl/Guaiacyl Content Using FT-Raman Spectroscopy and Partial Least Squares Modeling.
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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. |
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| Authors: | Lupoi, Jason jslupoi@lbl.gov, Healey, Adam1 adam.healey@uq.net.au, Singh, Seema seesing@sandia.gov, Sykes, Robert Robert.Sykes@nrel.gov, Davis, Mark Mark.Davis@nrel.gov, Lee, David2 dlee@usc.edu.au, Shepherd, Merv3 mervyn.shepherd@scu.edu.au, Simmons, Blake basimmons@lbl.gov, Henry, Robert1 robert.henry@uq.edu.au |
| Source: | BioEnergy Research. Sep2015, Vol. 8 Issue 3, p953-963. 11p. |
| Subjects: | Lignocellulose, Biomass chemicals, Feedstock, Least squares, Eucalyptus, Raman spectroscopy |
| 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. [ABSTRACT FROM AUTHOR] |
| Copyright of BioEnergy Research is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 109251508 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: High-Throughput Prediction of Acacia and Eucalypt Lignin Syringyl/Guaiacyl Content Using FT-Raman Spectroscopy and Partial Least Squares Modeling. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lupoi%2C+Jason%22">Lupoi, Jason</searchLink><i> jslupoi@lbl.gov</i><br /><searchLink fieldCode="AR" term="%22Healey%2C+Adam%22">Healey, Adam</searchLink><relatesTo>1</relatesTo><i> adam.healey@uq.net.au</i><br /><searchLink fieldCode="AR" term="%22Singh%2C+Seema%22">Singh, Seema</searchLink><i> seesing@sandia.gov</i><br /><searchLink fieldCode="AR" term="%22Sykes%2C+Robert%22">Sykes, Robert</searchLink><i> Robert.Sykes@nrel.gov</i><br /><searchLink fieldCode="AR" term="%22Davis%2C+Mark%22">Davis, Mark</searchLink><i> Mark.Davis@nrel.gov</i><br /><searchLink fieldCode="AR" term="%22Lee%2C+David%22">Lee, David</searchLink><relatesTo>2</relatesTo><i> dlee@usc.edu.au</i><br /><searchLink fieldCode="AR" term="%22Shepherd%2C+Merv%22">Shepherd, Merv</searchLink><relatesTo>3</relatesTo><i> mervyn.shepherd@scu.edu.au</i><br /><searchLink fieldCode="AR" term="%22Simmons%2C+Blake%22">Simmons, Blake</searchLink><i> basimmons@lbl.gov</i><br /><searchLink fieldCode="AR" term="%22Henry%2C+Robert%22">Henry, Robert</searchLink><relatesTo>1</relatesTo><i> robert.henry@uq.edu.au</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22BioEnergy+Research%22">BioEnergy Research</searchLink>. Sep2015, Vol. 8 Issue 3, p953-963. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Lignocellulose%22">Lignocellulose</searchLink><br /><searchLink fieldCode="DE" term="%22Biomass+chemicals%22">Biomass chemicals</searchLink><br /><searchLink fieldCode="DE" term="%22Feedstock%22">Feedstock</searchLink><br /><searchLink fieldCode="DE" term="%22Least+squares%22">Least squares</searchLink><br /><searchLink fieldCode="DE" term="%22Eucalyptus%22">Eucalyptus</searchLink><br /><searchLink fieldCode="DE" term="%22Raman+spectroscopy%22">Raman spectroscopy</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of BioEnergy Research is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s12155-015-9578-1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 953 Subjects: – SubjectFull: Lignocellulose Type: general – SubjectFull: Biomass chemicals Type: general – SubjectFull: Feedstock Type: general – SubjectFull: Least squares Type: general – SubjectFull: Eucalyptus Type: general – SubjectFull: Raman spectroscopy Type: general Titles: – TitleFull: High-Throughput Prediction of Acacia and Eucalypt Lignin Syringyl/Guaiacyl Content Using FT-Raman Spectroscopy and Partial Least Squares Modeling. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lupoi, Jason – PersonEntity: Name: NameFull: Healey, Adam – PersonEntity: Name: NameFull: Singh, Seema – PersonEntity: Name: NameFull: Sykes, Robert – PersonEntity: Name: NameFull: Davis, Mark – PersonEntity: Name: NameFull: Lee, David – PersonEntity: Name: NameFull: Shepherd, Merv – PersonEntity: Name: NameFull: Simmons, Blake – PersonEntity: Name: NameFull: Henry, Robert IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2015 Type: published Y: 2015 Identifiers: – Type: issn-print Value: 19391234 Numbering: – Type: volume Value: 8 – Type: issue Value: 3 Titles: – TitleFull: BioEnergy Research Type: main |
| ResultId | 1 |
