, Volume 23, Issue 3, pp 1565–1572 | Cite as

Assessment of endoglucanase activity by analyzing the degree of cellulose polymerization and high-throughput analysis by near-infrared spectroscopy

  • Yoshiki Horikawa
  • Tomoya Imai
  • Kentaro Abe
  • Keita Sakakibara
  • Yoshinobu Tsujii
  • Asako Mihashi
  • Yoshinori Kobayashi
  • Junji Sugiyama
Original Paper


We developed a novel and practical assessment technique for endoglucanase (EG) activity by measuring the degree of polymerization (DP) of cellulose from Eucalyptus globulus. This evaluation method demonstrated that EG II from Trichoderma reesei had higher endoglucanase activity than EG I, which has not been detected in conventional experiments using carboxymethyl cellulose as a model substrate. In addition, a high-throughput protocol for DP measurement was developed by using near-infrared spectroscopy combined with a multivariate analysis. Interpreting the regression coefficient, a reciprocal correlation was observed between the relative crystallinity of the cellulosic residue after enzymatic hydrolysis and the DP.


Endoglucanase Cellulose microfibril Degree of polymerization NIR spectroscopy Relative crystallinity 



Degree of polymerization








Carboxymethyl cellulose


Near-infrared spectroscopy


3,5-dinitrosalicylic acid




Gel permeation chromatography


Triglycine sulfate


Partial least squares


Coefficient of determination for calibration


Root-mean-square error of calibration


Coefficient of determination for prediction


Root-mean-square error of prediction


Polysaccharide monooxygenases


  1. Abdullah R, Saka S (2014) Hydrolysis behavior of various crystalline celluloses treated by cellulase of Tricoderma viride. Cellulose 21:4049–4058CrossRefGoogle Scholar
  2. Abe K, Iwamoto S, Yano H (2007) Obtaining cellulose nanofibers with a uniform width of 15 nm from wood. Biomacromolecules 8:3276–3278CrossRefGoogle Scholar
  3. Beeson WT, Phillips CM, Cate JHD, Marletta MA (2012) Oxidative cleavage of cellulose by fungal copper-dependent polysaccharide monooxygenases. J Am Chem Soc 134:890–892CrossRefGoogle Scholar
  4. Hallac BB, Sannigrahi P, Pu Y, Ray M, Murphy RJ, Ragauskas AJ (2009) Biomass characterization of Buddleja davidii: a potential feedstock for biofuel production. J Agric Food Chem 57:1275–1281CrossRefGoogle Scholar
  5. 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 105:5–16CrossRefGoogle Scholar
  6. Horikawa Y, Imai T, Takada R, Watanabe T, Takabe K, Kobayashi Y, Sugiyama J (2011) Near-Infrared chemometric approach to exhaustive analysis of rice straw pretreated for bioethanol conversion. Appl Biochem Biotechnol 164:194–203CrossRefGoogle Scholar
  7. Horikawa Y, Konakahara N, Imai T, Abe K, Kobayashi Y, Sugiyama J (2013) The structural changes in crystalline cellulose and effects on enzymatic digestibility. Polym Degrad Stab 98:2351–2356CrossRefGoogle Scholar
  8. Horikawa Y, Mizuno-Tazuru S, Sugiyama J (2015) Near-infrared spectroscopy as a potential method for identification of anatomically similar Japanese diploxylons. J Wood Sci 61:251–261CrossRefGoogle Scholar
  9. Inagaki T, Siesler HW, Mitsui K, Tsuchikawa S (2010) Difference of the crystal structure of cellulose in wood after hydrothermal and aging degradation: a NIR spectroscopy and XRD study. Biomacromolecules 11:2300–2305CrossRefGoogle Scholar
  10. Kanda T, Wakabayashi K, Nisizawa K (1976) Synergistic action of 2 different types of endo-cellulase components from Irpex lacteus (Polyporus tulipiferae) in hydrolysis of some insoluble celluloses. J Biochem 79:997–1006Google Scholar
  11. Kawai T, Nakazawa H, Ida N, Okada H, Tani S, Sumitani J, Kawaguchi T, Ogasawara W, Morikawa Y, Kobayashi Y (2012) Analysis of the saccharification capability of high-functional cellulase JN11 for various pretreated biomasses through a comparison with commercially available counterparts. J Ind Microbiol Biotechnol 39:1741–1749CrossRefGoogle Scholar
  12. Limayem A, Ricke SC (2012) Lignocellulosic biomass for bioethanol production: current perspectives, potential issues and future prospects. Prog Energy Combust Sci 38:449–467CrossRefGoogle Scholar
  13. Matthews JF, Skopec CE, Mason PE, Zuccato P, Torget RW, Sugiyama J, Himmel ME, Brady JW (2006) Computer simulation studies of microcrystalline cellulose Iβ. Carbohydr Res 341:138–152CrossRefGoogle Scholar
  14. Miller G (1959) Use of dinitrosalicylic acid reagent for determination of reducing sugar. Anal Chem 31:426–428CrossRefGoogle Scholar
  15. Nakazawa H, Kawai T, Ida N, Shida Y, Kobayashi Y, Okada H, Tani S, Sumitani J, Kawaguchi T, Morikawa Y, Ogasawara W (2012) Construction of a recombinant Trichoderma reesei strain expressing Aspergillus aculeatus β-glucosidase 1 for efficient biomass conversion. Biotechnol Bioeng 109:92–99CrossRefGoogle Scholar
  16. Nishiyama Y, Kim UJ, Kim DY, Katsumata KS, May RP, Langan P (2003) Periodic disorder along ramie cellulose microfibrils. Biomacromolecules 4:1013–1017CrossRefGoogle Scholar
  17. Sanchez OJ, Cardona CA (2008) Trends in biotechnological production of fuel ethanol from different feedstocks. Bioresour Technol 99:5270–5295CrossRefGoogle Scholar
  18. Sanderson MA, Agblevor F, Collins M, Johnson DK (1996) Compositional analysis of biomass feedstocks by near infrared reflectance spectroscopy. Biomass Bioenergy 11:365–370CrossRefGoogle Scholar
  19. Savitzky A, Golay MJE (1964) Smoothing and differentiation of data by simplified least squares procedures. Anal Chem 36:1627–1639CrossRefGoogle Scholar
  20. Sugiyama J (1996) Cellulose structure and its implication to biological diversity. Wood Res Tech Notes 32:16–22Google Scholar
  21. Tsuchikawa S, Siesler HW (2003) Near-infrared spectroscopic monitoring of the diffusion process of deuterium-labeled molecules in wood. Part I Softwood Appl Spectrosc 57:667–674CrossRefGoogle Scholar
  22. Wise L, Murphy M, D’Addieco A (1946) Chlorite holocellulose, its fractionation and bearing on summative wood analysis and studies on the hemicelluloses. Pap Trade J 122:35–43Google Scholar
  23. Ye XP, Liu L, Hayes D, Womac A, Hong KL, Sokhansanj S (2008) Fast classification and compositional analysis of cornstover fractions using fourier transform near-infrared techniques. Bioresour Technol 99:7323–7332CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Yoshiki Horikawa
    • 1
    • 2
  • Tomoya Imai
    • 2
  • Kentaro Abe
    • 2
  • Keita Sakakibara
    • 3
  • Yoshinobu Tsujii
    • 3
  • Asako Mihashi
    • 4
  • Yoshinori Kobayashi
    • 4
  • Junji Sugiyama
    • 2
    • 5
  1. 1.Faculty of AgricultureTokyo University of Agriculture and TechnologyFuchuJapan
  2. 2.Research Institute for Sustainable Humanosphere (RISH)Kyoto UniversityUjiJapan
  3. 3.Institute for Chemical ResearchKyoto UniversityUjiJapan
  4. 4.Tsukuba Research LaboratoryJapan Bioindustry AssociationTsukubaJapan
  5. 5.CREST-JSTKawaguchiJapan

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