Advertisement

BioEnergy Research

, Volume 8, Issue 3, pp 964–972 | Cite as

High-Throughput Method for Determining the Sugar Content in Biomass with Pyrolysis Molecular Beam Mass Spectrometry

  • Robert W. Sykes
  • Erica L. Gjersing
  • Crissa L. Doeppke
  • Mark F. Davis
Article

Abstract

There is an important need to assess biomass recalcitrance in large populations of both natural and transgenic plants to identify promising candidates for lignocellulosic biofuel production. In order to properly test and optimize parameters for biofuel production, the starting sugar content must be known to calculate percent sugar yield and conversion efficiencies. Pyrolysis molecular beam mass spectrometry (py-MBMS) has been used as a high-throughput method for determination of lignin content and structure, and this report demonstrates its applicability for determining glucose, xylose, arabinose, galactose, and mannose content in biomass. Biomass from conifers, hardwoods, and herbaceous species were used to create a 44 sample partial least squares (PLS) regression models of py-MBMS spectra-based sugar estimates on high-performance liquid chromatography (HPLC) sugar content data. The total sugar py-MBMS regression model had a R 2 of 0.91 with a 0.17 mg/mg root mean square error of validation indicating accurate estimation of total sugar content for a range of biomass types. Models were validated using eight independent biomass samples from multiple species, with predictions falling within errors of the HPLC data. With a data collection time of 1.5 min per sample, py-MBMS serves as a rapid high-throughput method for quantifying sugar content in biomass.

Keywords

Glucose Xylose Recalcitrance Prediction Herbaceous Conifer Hardwood Bioenergy 

Notes

Acknowledgments

This work was conducted as part of the BioEnergy Science Center (BESC). The BESC is a US Department of Energy Bioenergy Research Center supported by the Office of Biological and Environmental Research in the DOE Office of Science. This work was supported by the US Department of Energy under contract no. DE-AC36-08-GO28308 with the National Renewable Energy Laboratory.

Conflict of Interest

The authors declare that they have no competing interests.

References

  1. 1.
    Menon V, Rao M (2012) Trends in bioconversion of lignocellulose: biofuels, platform chemicals & biorefinery concept. Prog Energy Combust Sci 38(4):522–550. doi: 10.1016/j.pecs.2012.02.002 CrossRefGoogle Scholar
  2. 2.
    Lange J-P, van der Heide E, van Buijtenen J, Price R (2012) Furfural—a promising platform for lignocellulosic biofuels. ChemSusChem 5(1):150–166. doi: 10.1002/cssc.201100648 CrossRefPubMedGoogle Scholar
  3. 3.
    Slutier A, Hames B, Ruiz R, Scarlata C, Sluiter J, Templeton D, Crocker D (2008) Determination of structural carbohydrates and lignin in biomass. National Renewable Energy Laboratory, GoldenGoogle Scholar
  4. 4.
    Slutier A, Hames B, Ruiz R, Scarlata C, Sluiter J, Templeton D (2006) Determination of sugars, byproducts, and degradation products in liquid fraction process samples. National Renewable Energy Laboratory, GoldenGoogle Scholar
  5. 5.
    Sluiter J, Sluiter A (2011) Summative mass closure. National Renewable Energy Laboratory, GoldenGoogle Scholar
  6. 6.
    Slutier A, Ruiz R, Scarlata C, Sluiter J, Templeton D (2005) Determination of extractives in biomass. National Renewable Energy Laboratory, GoldenGoogle Scholar
  7. 7.
    Hames B, Ruiz R, Scarlata C, Sluiter A, Sluiter J, Templeton D (2008) Preparation of samples for compositional analysis. National Renewable Energy Laboratory, GoldenGoogle Scholar
  8. 8.
    Slutier A, Hames B, Ruiz R, Scarlata C, Sluiter J, Templeton D (2008) Determinatio of ash in biomass. National Renewable Energy Laboratory, GoldenGoogle Scholar
  9. 9.
    Lupoi JS, Singh S, Simmons BA, Henry RJ (2013) Assessment of lignocellulosic biomass using analytical spectroscopy: an evolution to high-throughput techniques. BioEnergy Res 7(1):1–23. doi: 10.1007/s12155-013-9352-1 CrossRefGoogle Scholar
  10. 10.
    Gjersing E, Happs R, Doeppke C, Sykes R, Davis MF (2013) Rapid determination fo components found in biomass hydrolysates using nuclear magnetic resonance spectroscopy. Biotech Bioeng 110(3):7CrossRefGoogle Scholar
  11. 11.
    Templeton D, Sluiter A, Hayward T, Hames B, Thomas S (2009) Assessing corn stover composition and sources of variability via NIRS. Cellulose 16(4):621–639. doi: 10.1007/s10570-009-9325-x CrossRefGoogle Scholar
  12. 12.
    Wolfrum E, Sluiter A (2009) Improved multivariate calibration models for corn stover feedstock and dilute-acid pretreated corn stover. Cellulose 16(4):567–576. doi: 10.1007/s10570-009-9320-2 CrossRefGoogle Scholar
  13. 13.
    Wilks P (2006) NIR versus mid-IR: how to choose. Spectroscopy (Duluth, MN, U S) 21(4):43–46Google Scholar
  14. 14.
    Delwiche SR, Pitt RE, Norris KH (1992) Sensitivity of near-infrared absorption to moisture content versus water activity in starch and cellulose. Cereal Chem 69(1):107–109Google Scholar
  15. 15.
    Agblevor FA, Evans RJ, Johnson KD (1994) Molecular-beam mass-spectrometric analysis of lignocellulosic materials. I. Herbaceous biomass. J Anal Appl Pyrolysis 30(2):125–144. doi: 10.1016/0165-2370(94)00808-6 CrossRefGoogle Scholar
  16. 16.
    Kelley SS, Rowell RM, Davis M, Jurich CK, Ibach R (2004) Rapid analysis of the chemical composition of agricultural fibers using near infrared spectroscopy and pyrolysis molecular beam mass spectrometry. Biomass Bioenergy 27(1):77–88. doi: 10.1016/j.biombioe.2003.11.005 CrossRefGoogle Scholar
  17. 17.
    Evans RJ, Wang D, Agblevor FA, Chum HL, Baldwin SD (1996) Mass spectrometric studies of the thermal decomposition of carbohydrates using 13C-labeled cellulose and glucose. Carbohydr Res 281(2):219–235CrossRefPubMedGoogle Scholar
  18. 18.
    Schlichting GJ, Shin E-J, McKibben SR, Dibenedetto J, Evans RJ, Czernik SR, Herring AM (2009) Effect of feedstock variability on fast pyrolysis oil production and characterization for distributed reforming. Am Chem Soc FUEL-247Google Scholar
  19. 19.
    Sykes R, Yung M, Novaes E, Kirst M, Peter G, Davis M (2009) High-throughput screening of plant cell-wall composition using pyrolysis molecular beam mass spectroscopy, vol 581. Biofuels: methods and protocols, methods in molecular biology. Humana Press, New YorkGoogle Scholar
  20. 20.
    Baxter HL, Mazarei M, Labbe N, Kline LM, Cheng Q, Windham MT, Mann DGJ, Fu C, Ziebell A, Sykes RW, Rodriguez M Jr, Davis MF, Mielenz JR, Dixon RA, Wang Z-Y, Stewart CN Jr (2014) Two-year field analysis of reduced recalcitrance transgenic switchgrass. Plant Biotechnol J 12(7):914–924CrossRefPubMedGoogle Scholar
  21. 21.
    Penning BW, Sykes RW, Babcock NC, Dugard CK, Klimek JF, Gamblin D, Davis M, Filley TR, Mosier NS, Weil CF, McCann MC, Carpita NC (2014) Validation of PyMBMS as a high-throughput screen for lignin abundance in lignocellulosic biomass of grasses. BioEnergy Res 7(3):899–908. doi: 10.1007/s12155-014-9410-3 CrossRefGoogle Scholar
  22. 22.
    Decker SR, Carlile M, Selig MJ, Doeppke C, Davis M, Sykes R, Turner G, Ziebell A (2012) Reducing the effect of variable starch levels in biomass recalcitrance screening, vol 908. Biomass conversion: methods and protocols, methods in molecular biology. Humana Press, New YorkGoogle Scholar
  23. 23.
    Evans RJ, Milne TA (1987) Molecular characterization of the pyrolysis of biomass. Energy Fuels 1(2):123–137CrossRefGoogle Scholar
  24. 24.
    Tuskan G, West D, Bradshaw HD, Neale D, Sewell M, Wheeler N, Megraw B, Jech K, Wiselogel A, Evans R, Elam C, Davis M, Dinus R (1999) Two high-throughput techniques for determining wood properties as part of a molecular genetics analysis of hybrid poplar and loblolly pine. Appl Biochem Biotechnol 77(1–3):55–65CrossRefGoogle Scholar
  25. 25.
    Lin SY, Dence CW, Editors (1994) Methods in lignin chemistry. vol Copyright (C) 2014 American Chemical Society (ACS). All Rights Reserved. Uni Publishers Co., Ltd.,Google Scholar
  26. 26.
    Xu F, Zhou L, Zhang K, Yu J, Wang D (2014) Rapid determination of both structural polysaccharides and soluble sugars in sorghum biomass using near-infrared spectroscopy. BioEnergy Res. doi: 10.1007/s12155-014-9511-z Google Scholar
  27. 27.
    Petrik DL, Karlen SD, Cass CL, Padmakshan D, Lu F, Liu S, Le Bris P, Antelme S, Santoro N, Wilkerson CG, Sibout R, Lapierre C, Ralph J, Sedbrook JC (2014) p-Coumaroyl-CoA: monolignol transferase (PMT) acts specifically in the lignin biosynthetic pathway in Brachypodium distachyon. Plant J 77(5):713–726. doi: 10.1111/tpj.12420 PubMedCentralCrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York (outside the USA) 2015

Authors and Affiliations

  1. 1.National Bioenergy CenterNational Renewable Energy LaboratoryGoldenUSA
  2. 2.BioEnergy Science CenterGoldenUSA

Personalised recommendations