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Evaluating lignocellulosic biomass, its derivatives, and downstream products with Raman spectroscopy.

Lupoi JS, Gjersing E, Davis MF - Front Bioeng Biotechnol (2015)

Bottom Line: Hydrolysis and fermentation products, such as glucose and ethanol, can be quantified off-, at-, or on-line.Raman imaging has enabled researchers to develop a visual understanding of reactions, such as different pretreatment strategies, in real-time, while also providing integral chemical information.This review provides an overview of what Raman spectroscopy is, and how it has been applied to the analysis of whole lignocellulosic biomass, its derivatives, and downstream process monitoring.

View Article: PubMed Central - PubMed

Affiliation: Oak Ridge National Laboratory, BioEnergy Science Center , Oak Ridge, TN , USA ; National Renewable Energy Laboratory, National Bioenergy Center , Golden, CO , USA.

ABSTRACT
The creation of fuels, chemicals, and materials from plants can aid in replacing products fabricated from non-renewable energy sources. Before using biomass in downstream applications, it must be characterized to assess chemical traits, such as cellulose, lignin, or lignin monomer content, or the sugars released following an acid or enzymatic hydrolysis. The measurement of these traits allows researchers to gage the recalcitrance of the plants and develop efficient deconstruction strategies to maximize yields. Standard methods for assessing biomass phenotypes often have experimental protocols that limit their use for screening sizeable numbers of plant species. Raman spectroscopy, a non-destructive, non-invasive vibrational spectroscopy technique, is capable of providing qualitative, structural information and quantitative measurements. Applications of Raman spectroscopy have aided in alleviating the constraints of standard methods by coupling spectral data with multivariate analysis to construct models capable of predicting analytes. Hydrolysis and fermentation products, such as glucose and ethanol, can be quantified off-, at-, or on-line. Raman imaging has enabled researchers to develop a visual understanding of reactions, such as different pretreatment strategies, in real-time, while also providing integral chemical information. This review provides an overview of what Raman spectroscopy is, and how it has been applied to the analysis of whole lignocellulosic biomass, its derivatives, and downstream process monitoring.

No MeSH data available.


Related in: MedlinePlus

Comparison of 785 and 1064 nm excitation wavelengths to evaluate lignin. (A) Background-subtracted Raman spectra of 50 mg/mL lignin, dissolved in methanol, obtained using a dispersive 785 nm (gray) or 1064 nm (black) spectrometer. The 785 nm excitation spectrum has been divided by 60. (B) The 1064 nm excitation lignin spectrum, plotted on a smaller scale to elucidate spectral features [reprinted with permission from Elsevier, Meyer et al. (2011)].
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Figure 3: Comparison of 785 and 1064 nm excitation wavelengths to evaluate lignin. (A) Background-subtracted Raman spectra of 50 mg/mL lignin, dissolved in methanol, obtained using a dispersive 785 nm (gray) or 1064 nm (black) spectrometer. The 785 nm excitation spectrum has been divided by 60. (B) The 1064 nm excitation lignin spectrum, plotted on a smaller scale to elucidate spectral features [reprinted with permission from Elsevier, Meyer et al. (2011)].

Mentions: As previously mentioned, NIR dispersive Raman spectroscopy can provide a suitable, less costly alternative to FT-Raman spectroscopy. Despite this, there are relatively few instances of researchers using this instrumental configuration (Roder and Sixta, 2005; Shih and Smith, 2009; Li et al., 2010, 2011, 2013; Meyer et al., 2011; Shih et al., 2011; Zakzeski et al., 2011; Lupoi and Smith, 2012; Ewanick et al., 2013; Gray et al., 2013; Azimvand, 2014; Iversen et al., 2014). A comparison between 785 and 1064 nm excitation sources revealed the latter to provide better signal-to-noise (S/N) when measuring hydrolytic lignin using home-built Raman spectrometers (Figure 2) (Meyer et al., 2011). The spectrum generated using the 785 nm laser exhibited a broad, featureless fluorescence background (Figure 3). The fluorescence emission peak maximum is expected to be in the visible region of the electromagnetic spectrum. When excited with the 785 nm light, however, a low intensity peak was detected that resembled the background measured in the Raman spectrum. Although the intensities of the peaks generated using the 1064 nm laser were relatively weak, the fluorescence was virtually eliminated (Figure 3). This instrumental configuration also provided higher S/N when compared to a commercial FT-Raman spectrometer using acquisition times greater than 15 s. The same system was used to develop a principal component regression (PCR) model to predict the S and G lignin content of a diverse assortment of feedstocks, including Miscanthus, switchgrass, poplar, and pine (Lupoi and Smith, 2012). The model was constructed from Raman spectral data conjoined with thioacidolysis/GCMS S and G lignin percentages.


Evaluating lignocellulosic biomass, its derivatives, and downstream products with Raman spectroscopy.

Lupoi JS, Gjersing E, Davis MF - Front Bioeng Biotechnol (2015)

Comparison of 785 and 1064 nm excitation wavelengths to evaluate lignin. (A) Background-subtracted Raman spectra of 50 mg/mL lignin, dissolved in methanol, obtained using a dispersive 785 nm (gray) or 1064 nm (black) spectrometer. The 785 nm excitation spectrum has been divided by 60. (B) The 1064 nm excitation lignin spectrum, plotted on a smaller scale to elucidate spectral features [reprinted with permission from Elsevier, Meyer et al. (2011)].
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4403602&req=5

Figure 3: Comparison of 785 and 1064 nm excitation wavelengths to evaluate lignin. (A) Background-subtracted Raman spectra of 50 mg/mL lignin, dissolved in methanol, obtained using a dispersive 785 nm (gray) or 1064 nm (black) spectrometer. The 785 nm excitation spectrum has been divided by 60. (B) The 1064 nm excitation lignin spectrum, plotted on a smaller scale to elucidate spectral features [reprinted with permission from Elsevier, Meyer et al. (2011)].
Mentions: As previously mentioned, NIR dispersive Raman spectroscopy can provide a suitable, less costly alternative to FT-Raman spectroscopy. Despite this, there are relatively few instances of researchers using this instrumental configuration (Roder and Sixta, 2005; Shih and Smith, 2009; Li et al., 2010, 2011, 2013; Meyer et al., 2011; Shih et al., 2011; Zakzeski et al., 2011; Lupoi and Smith, 2012; Ewanick et al., 2013; Gray et al., 2013; Azimvand, 2014; Iversen et al., 2014). A comparison between 785 and 1064 nm excitation sources revealed the latter to provide better signal-to-noise (S/N) when measuring hydrolytic lignin using home-built Raman spectrometers (Figure 2) (Meyer et al., 2011). The spectrum generated using the 785 nm laser exhibited a broad, featureless fluorescence background (Figure 3). The fluorescence emission peak maximum is expected to be in the visible region of the electromagnetic spectrum. When excited with the 785 nm light, however, a low intensity peak was detected that resembled the background measured in the Raman spectrum. Although the intensities of the peaks generated using the 1064 nm laser were relatively weak, the fluorescence was virtually eliminated (Figure 3). This instrumental configuration also provided higher S/N when compared to a commercial FT-Raman spectrometer using acquisition times greater than 15 s. The same system was used to develop a principal component regression (PCR) model to predict the S and G lignin content of a diverse assortment of feedstocks, including Miscanthus, switchgrass, poplar, and pine (Lupoi and Smith, 2012). The model was constructed from Raman spectral data conjoined with thioacidolysis/GCMS S and G lignin percentages.

Bottom Line: Hydrolysis and fermentation products, such as glucose and ethanol, can be quantified off-, at-, or on-line.Raman imaging has enabled researchers to develop a visual understanding of reactions, such as different pretreatment strategies, in real-time, while also providing integral chemical information.This review provides an overview of what Raman spectroscopy is, and how it has been applied to the analysis of whole lignocellulosic biomass, its derivatives, and downstream process monitoring.

View Article: PubMed Central - PubMed

Affiliation: Oak Ridge National Laboratory, BioEnergy Science Center , Oak Ridge, TN , USA ; National Renewable Energy Laboratory, National Bioenergy Center , Golden, CO , USA.

ABSTRACT
The creation of fuels, chemicals, and materials from plants can aid in replacing products fabricated from non-renewable energy sources. Before using biomass in downstream applications, it must be characterized to assess chemical traits, such as cellulose, lignin, or lignin monomer content, or the sugars released following an acid or enzymatic hydrolysis. The measurement of these traits allows researchers to gage the recalcitrance of the plants and develop efficient deconstruction strategies to maximize yields. Standard methods for assessing biomass phenotypes often have experimental protocols that limit their use for screening sizeable numbers of plant species. Raman spectroscopy, a non-destructive, non-invasive vibrational spectroscopy technique, is capable of providing qualitative, structural information and quantitative measurements. Applications of Raman spectroscopy have aided in alleviating the constraints of standard methods by coupling spectral data with multivariate analysis to construct models capable of predicting analytes. Hydrolysis and fermentation products, such as glucose and ethanol, can be quantified off-, at-, or on-line. Raman imaging has enabled researchers to develop a visual understanding of reactions, such as different pretreatment strategies, in real-time, while also providing integral chemical information. This review provides an overview of what Raman spectroscopy is, and how it has been applied to the analysis of whole lignocellulosic biomass, its derivatives, and downstream process monitoring.

No MeSH data available.


Related in: MedlinePlus