Advertisement

BioEnergy Research

, Volume 4, Issue 2, pp 96–110 | Cite as

Quantifying Actual and Theoretical Ethanol Yields for Switchgrass Strains Using NIRS Analyses

  • Kenneth P. VogelEmail author
  • Bruce S. Dien
  • Hans G. Jung
  • Michael D. Casler
  • Steven D. Masterson
  • Robert B. Mitchell
Article

Abstract

Quantifying actual and theoretical ethanol yields from biomass conversion processes such as simultanteous saccharification and fermentation (SSF) requires expensive, complex fermentation assays, and extensive compositional analyses of the biomass sample. Near-infrared reflectance spectroscopy (NIRS) is a non-destructive technology that can be used to obtain rapid, low-cost, high-throughput, and accurate estimates of agricultural product composition. In this study, broad-based NIRS calibrations were developed for switchgrass biomass that can be used to estimate over 20 components including cell wall and soluble sugars and also ethanol production and pentose sugars released as measured using a laboratory SSF procedure. With this information, an additional 13 complex feedstock traits can be determined including theoretical and actual ethanol yields from hexose fermentation. The NIRS calibrations were used to estimate feedstock composition and conversion information for biomass samples from a multi-year switchgrass (Panicum virgatum L.) biomass cultivar evaluation trial. There were significant differences among switchgrass strains for all biomass conversion and composition traits including actual ethanol yields, ETOHL (L Mg−1) and theoretical ethanol yields, ETOHTL (L Mg−1), based on cell wall and non-cell wall composition NIRS analyses. ETOHL means ranged from 98 to 115 L Mg−1 while ETOHTL means ranged from 203 to 222 L Mg−1. Because of differences in both biomass yields and conversion efficiency, there were significant differences among strains for both actual (2,534–3,720 L ha−1) and theoretical (4,878–7,888 L ha−1) ethanol production per hectare. It should be feasible to improve ethanol yields per hectare by improving both biomass yield and conversion efficiency by using NIRS analyses to quantify differences among cultivars and management practices.

Keywords

Switchgrass Biomass Ethanol NIRS Quality 

References

  1. 1.
    Vogel KP, Jung HG (2001) Genetic modification of herbaceous plants for feed and fuel. Crit Rev Plant Sci 20:15–49Google Scholar
  2. 2.
    Roberts CA, Workman, J Jr, Reeves JB III (eds.) (2004) Near-infrared spectroscopy in agriculture. Agron Monog 44. ASA, CSSA, and SSSA, Madison, WI.Google Scholar
  3. 3.
    Westerhaus M, Workman J, Reeves JB III, Mark H (2004). Quantitative analysis. In: Roberts CA, Workman J. Jr., Reeves JB (eds) Near-infrared spectroscopy in agriculture. Agron. Monog. 44. ASA, CSSA, and SSSA, Madison, WI, p 133–174Google Scholar
  4. 4.
    Shenk JS, Westerhaus MO (1991) Population definition, sample selection, and calibration procedures for near-infrared reflectance spectroscopy. Crop Sci 31:469–474CrossRefGoogle Scholar
  5. 5.
    Wold S, Geladi P, Esbensen K, Öhman J (1987) Multi-way principal components and PLS analysis. J Chemom 1:41–56CrossRefGoogle Scholar
  6. 6.
    Wold S, Kettanehwold N, Skagerberg B (1989) Nonlinear PLS modeling. Chemom Intell Lab Syst 7:53–65CrossRefGoogle Scholar
  7. 7.
    Himmel ME (2007) Biomass recalcitrance: engineering plants and enzymes for biofuels production. Science 306:982Google Scholar
  8. 8.
    Yanase H, Yamamoto K, Matsuda S, Yamamoto S, Okamoto K (2007) Genetic engineering of Zymobacter palmae for production of ethanol from xylose. Appl Environ Microbiol 73:2592–2599PubMedCrossRefGoogle Scholar
  9. 9.
    Sanderson MA, Agblevor F, Collins M, Johnson DK (1966) Compositional analysis of biomass feedstocks by near infrared reflectance spectroscopy. Biomass and Bioenergy 111:365–370Google Scholar
  10. 10.
    Maranan MC, Laborie MPG (2007) Analysis of energy traits of Populus spp. clones by near-infrared spectroscopy. J Biobased Mater Bioenergy 1:155–165Google Scholar
  11. 11.
    Hames BR, Thomas SR, Sluiter AD, Roth CJ, Templeton DW (2003) Rapid biomass analysis—new tools for compositional analysis of corn stover feedstocks and process intermediates from ethanol production. Appl Biochem Biotechnol 105:5–16PubMedCrossRefGoogle Scholar
  12. 12.
    Lamb JFS, Jung HG, Sheaffer CC, Samac DA (2007) Alfalfa leaf protein and stem cell wall polysaccharide yields under hay and biomass management systems. Crop Sci 47:1407–1415CrossRefGoogle Scholar
  13. 13.
    Lorenz AJ, Coors JG, de Loen N, Wolfurm EJ, Hames BR, Sluiter AD, Weimer PJ (2009) Characterization, genetic variation, and combining ability of maize traits relevant to the production of cellulosic ethanol. Crop Sci 59:85–98CrossRefGoogle Scholar
  14. 14.
    Lorenzana RE, Lewis MF, Jung HG, Bernardo R (2010) Quantitative trait loci and trait correlations for maize stover cell wall composition and glucose release for cellulosic ethanol. Crop Sci 50:541–555CrossRefGoogle Scholar
  15. 15.
    Vogel KP, Brejda JJ, Walters DT, Buxton DR (2002) Switchgrass biomass production in the Midwest USA: harvest and nitrogen management. Agron J 94:413–420CrossRefGoogle Scholar
  16. 16.
    Casler MD, Vogel KP, Taliaferro CM, Wynia RE (2004) Latitudinal adaptation of switchgrass populations. Crop Sci 44:293–403Google Scholar
  17. 17.
    Moore KJ, Moser LE, Vogel KP, Waller SS, Johnson BE, Pedersen JF (1991) Describing and quantifying growth stages of perennial forage grasses. Agron J 83:1073–1077CrossRefGoogle Scholar
  18. 18.
    Varvel GE, Vogel KP, Mitchell RB, Follett RF, Kimble JM (2008) Comparison of corn and switchgrass on marginal soils for bioenergy. Biomass and Bioenergy 32:18–21CrossRefGoogle Scholar
  19. 19.
    Vogel KP, Mitchell RB (2008) Heterosis in switchgrass: biomass yield in swards. Crop Sci 48:2159–2164CrossRefGoogle Scholar
  20. 20.
    Murray I, Cowe, I (2004) Sample preparation. In: Roberts CA, Workman J Jr, Reeves JB III (eds) Near-infrared spectroscopy in agriculture. Agron Monog 44. ASA, CSSA, and SSSA, Madison, WI, p 75–112Google Scholar
  21. 21.
    Dien BS (2010) Mass balances and analytical methods for biomass pre-treatment experiments. In: Vertes A, Qureshi N, Yukawa H, Blaschek H (eds) Biomass to biofuels: strategies for global industries. Wiley, United Kingdom, pp 213–231CrossRefGoogle Scholar
  22. 22.
    Bremner JM (1996) Nitrogen - Total. p. 1085–1121. In Sparks DL et al. (ed.) Methods of soil analysis. Part 3. Chemical methods. SSSA Book Ser. 5. SSSA and ASA, Madison, WI.Google Scholar
  23. 23.
    Watson M E, Issac RA (1990) Analytical instruments for soil and plant analysis. In Westerman R (ed) Soil testing and plant analysis. (3rd Ed.). SSSA Book Ser. 3. SSSA, Madison, WI, p 691–704Google Scholar
  24. 24.
    Padmore JM (1990a) Fat (crude) or ether extract in animal feed. Method 920.39. In: Herlich K (ed). Official methods of analysis of Association of Official Analytical Chemists. 15th ed. Arlington, VA.Google Scholar
  25. 25.
    Dien BS, Jung HG, Vogel KP, Casler MD, Lamb JFS, Weimer PJ, Iten L, Mitchell RB, Sarath G (2006) Chemical composition and response to dilute-acid pretreatment and enzymatic saccharification of alfalfa, reed canarygrass, and switchgrass. Biomass Bioenergy 30:880–891CrossRefGoogle Scholar
  26. 26.
    Smith D (1973) The nonstructural carbohydrates. In: Butler GW, Bailey RW (eds) Chemistry and biochemistry of herbage, vol 1. Academic, New York, pp 105–155Google Scholar
  27. 27.
    Theander O, Aman P, Westerlund E, Andersson R, Pettersson D (1995) Total dietary fiber determined as neutral sugar residues, uronic acid residues, and Klason lignin (the Uppsala method): collaborative study. J AOAC Int 78:1030–1044PubMedGoogle Scholar
  28. 28.
    Ahmed AER, Labavitch JM (1977) A simplified method for accurate determination of cell wall uronide content. J Food Biochem 1:361–365CrossRefGoogle Scholar
  29. 29.
    Iiyama K, Lam TBT, Stone BA (1990) Phenolic acid bridges between olysaccharides and lignin in wheat internodes. Phytochemistry 29:733–737CrossRefGoogle Scholar
  30. 30.
    Jung HG, Shalita-Jones SC (1990) Variation in the extractability of esterified p-coumaric and ferulic acids from forage cell walls. J Agric Food Chem 38:397–402CrossRefGoogle Scholar
  31. 31.
    Dowe N, McMillan J (2001) SSF Experimental Protocols—Lignocellulosic Biomass Hydrolysis and Fermentation; Laboratory Analytical Procedure (LAP). National Renewable Energy Laboratory, NREL/TP-510-42630. Available at http://www.nrel.gov/biomass/pdfs/42630.pdf. Accessed 08 Jan 09.
  32. 32.
    Dien BS, Nagle N, Hicks KB, Singh V, Moreau RA, Tucker MP et al (2004) Fermentation of "Quick Fiber" produced from a modified corn-milling process into ethanol and recovery of corn fiber. Appl Biochem Biotechnol 113–116:937–949PubMedCrossRefGoogle Scholar
  33. 33.
    Padmore JM (1990b) Protein (crude) in animal feed Dumas method. Method 968.06. In: Herlich K (eds) Official methods of analysis of Association of Official Analytical Chemists. 15th ed. Arlington, VAGoogle Scholar
  34. 34.
    Vogel KP, Pedersen JF, Masterson SD, Toy JJ (1999) Evaluation of a filter bag system for NDF, ADF, and IVDMD forage analysis. Crop Sci 39:276–279CrossRefGoogle Scholar
  35. 35.
    SAS (2000-2004) SAS 9.1.3 help and documentation. SAS Institute Inc, CaryGoogle Scholar
  36. 36.
    Williams PC (1987) Variables affecting near-infrared reflectance spectroscopic analysis. In: Williams P, Norris K (eds) Near-infrared technology in the agriculture and food industries. 1st Ed. Am Cereal Assoc Cereal Chem, St. Paul, MN. p 143–167Google Scholar

Copyright information

© US Government 2010

Authors and Affiliations

  • Kenneth P. Vogel
    • 1
    Email author
  • Bruce S. Dien
    • 2
  • Hans G. Jung
    • 3
  • Michael D. Casler
    • 4
  • Steven D. Masterson
    • 1
  • Robert B. Mitchell
    • 1
  1. 1.Grain, Forage, and Bioenergy Research Unit, Agricultural Research Service, US Department of Agriculture (USDA-ARS)University of NebraskaLincolnUSA
  2. 2.Fermentation Biotechnology Research UnitNational Center for Agricultural Utilization Research, USDA-ARSPeoriaUSA
  3. 3.Plant Science Research Unit, USDA-ARSSt. PaulUSA
  4. 4.US Dairy Forage Research CenterMadisonUSA

Personalised recommendations