Characterization of woody and herbaceous biomasses lignin composition with 1064 nm dispersive multichannel Raman spectroscopy.
Identifieur interne : 002B66 ( Main/Exploration ); précédent : 002B65; suivant : 002B67Characterization of woody and herbaceous biomasses lignin composition with 1064 nm dispersive multichannel Raman spectroscopy.
Auteurs : Jason S. Lupoi [États-Unis] ; Emily A. SmithSource :
- Applied spectroscopy [ 1943-3530 ] ; 2012.
Descripteurs français
- KwdFr :
- Analyse spectrale Raman (méthodes), Biomasse (MeSH), Bois (composition chimique), Chromatographie gazeuse-spectrométrie de masse (MeSH), Facteurs temps (MeSH), Hibiscus (composition chimique), Lignine (analyse), Lignine (composition chimique), Plantes (composition chimique), Poaceae (composition chimique), Résonance magnétique nucléaire biomoléculaire (MeSH), Trifolium (composition chimique).
- MESH :
- analyse : Lignine.
- composition chimique : Bois, Hibiscus, Lignine, Plantes, Poaceae, Trifolium.
- méthodes : Analyse spectrale Raman.
- Biomasse, Chromatographie gazeuse-spectrométrie de masse, Facteurs temps, Résonance magnétique nucléaire biomoléculaire.
English descriptors
- KwdEn :
- Biomass (MeSH), Gas Chromatography-Mass Spectrometry (MeSH), Hibiscus (chemistry), Lignin (analysis), Lignin (chemistry), Nuclear Magnetic Resonance, Biomolecular (MeSH), Plants (chemistry), Poaceae (chemistry), Spectrum Analysis, Raman (methods), Time Factors (MeSH), Trifolium (chemistry), Wood (chemistry).
- MESH :
- chemical , analysis : Lignin.
- chemistry : Hibiscus, Lignin, Plants, Poaceae, Trifolium, Wood.
- methods : Spectrum Analysis, Raman.
- Biomass, Gas Chromatography-Mass Spectrometry, Nuclear Magnetic Resonance, Biomolecular, Time Factors.
Abstract
Biomass representing different classes of bioenergy feedstocks, including woody and herbaceous species, was measured with 1064 nm Raman spectroscopy. Pine, oak, poplar, kenaf, miscanthus, pampas grass, switchgrass, alfalfa, orchard grass, and red clover were included in this study. Spectral differences have been identified with an emphasis on lignin guaiacyl and syringyl monomer content and carotenoid compounds. The interpretation of the Raman spectra was correlated with (13)C-nuclear magnetic resonance cross-polarization/magic-angle spinning spectra of select biomass samples. Thioacidolysis quantification of guaiacyl and syringyl monomer composition and the library of Raman spectra were used as a training set to develop a principal component analysis model for classifying plant samples and a principal component regression model for quantifying lignin guaiacyl and syringyl composition. Raman spectroscopy with 1064 nm excitation offers advantages over alternative techniques for biomass characterization, including low spectral backgrounds, higher spectral resolution, short analysis times, and nondestructive analyses.
DOI: 10.1366/12-06621
PubMed: 22800567
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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<affiliation wicri:level="1"><nlm:affiliation>U.S. Department of Energy, Ames Laboratory, Ames, Iowa 50011, USA.</nlm:affiliation>
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Biomass (MeSH)</term>
<term>Gas Chromatography-Mass Spectrometry (MeSH)</term>
<term>Hibiscus (chemistry)</term>
<term>Lignin (analysis)</term>
<term>Lignin (chemistry)</term>
<term>Nuclear Magnetic Resonance, Biomolecular (MeSH)</term>
<term>Plants (chemistry)</term>
<term>Poaceae (chemistry)</term>
<term>Spectrum Analysis, Raman (methods)</term>
<term>Time Factors (MeSH)</term>
<term>Trifolium (chemistry)</term>
<term>Wood (chemistry)</term>
</keywords>
<keywords scheme="KwdFr" xml:lang="fr"><term>Analyse spectrale Raman (méthodes)</term>
<term>Biomasse (MeSH)</term>
<term>Bois (composition chimique)</term>
<term>Chromatographie gazeuse-spectrométrie de masse (MeSH)</term>
<term>Facteurs temps (MeSH)</term>
<term>Hibiscus (composition chimique)</term>
<term>Lignine (analyse)</term>
<term>Lignine (composition chimique)</term>
<term>Plantes (composition chimique)</term>
<term>Poaceae (composition chimique)</term>
<term>Résonance magnétique nucléaire biomoléculaire (MeSH)</term>
<term>Trifolium (composition chimique)</term>
</keywords>
<keywords scheme="MESH" type="chemical" qualifier="analysis" xml:lang="en"><term>Lignin</term>
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<keywords scheme="MESH" qualifier="analyse" xml:lang="fr"><term>Lignine</term>
</keywords>
<keywords scheme="MESH" qualifier="chemistry" xml:lang="en"><term>Hibiscus</term>
<term>Lignin</term>
<term>Plants</term>
<term>Poaceae</term>
<term>Trifolium</term>
<term>Wood</term>
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<keywords scheme="MESH" qualifier="composition chimique" xml:lang="fr"><term>Bois</term>
<term>Hibiscus</term>
<term>Lignine</term>
<term>Plantes</term>
<term>Poaceae</term>
<term>Trifolium</term>
</keywords>
<keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Spectrum Analysis, Raman</term>
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<keywords scheme="MESH" qualifier="méthodes" xml:lang="fr"><term>Analyse spectrale Raman</term>
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<keywords scheme="MESH" xml:lang="en"><term>Biomass</term>
<term>Gas Chromatography-Mass Spectrometry</term>
<term>Nuclear Magnetic Resonance, Biomolecular</term>
<term>Time Factors</term>
</keywords>
<keywords scheme="MESH" xml:lang="fr"><term>Biomasse</term>
<term>Chromatographie gazeuse-spectrométrie de masse</term>
<term>Facteurs temps</term>
<term>Résonance magnétique nucléaire biomoléculaire</term>
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<front><div type="abstract" xml:lang="en">Biomass representing different classes of bioenergy feedstocks, including woody and herbaceous species, was measured with 1064 nm Raman spectroscopy. Pine, oak, poplar, kenaf, miscanthus, pampas grass, switchgrass, alfalfa, orchard grass, and red clover were included in this study. Spectral differences have been identified with an emphasis on lignin guaiacyl and syringyl monomer content and carotenoid compounds. The interpretation of the Raman spectra was correlated with (13)C-nuclear magnetic resonance cross-polarization/magic-angle spinning spectra of select biomass samples. Thioacidolysis quantification of guaiacyl and syringyl monomer composition and the library of Raman spectra were used as a training set to develop a principal component analysis model for classifying plant samples and a principal component regression model for quantifying lignin guaiacyl and syringyl composition. Raman spectroscopy with 1064 nm excitation offers advantages over alternative techniques for biomass characterization, including low spectral backgrounds, higher spectral resolution, short analysis times, and nondestructive analyses.</div>
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