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Development of Chemometric Model for Characterization of Non-wood by FT-NIR Data

  • Corresponding author: Mohammad Nashir Uddin, m2nashir@yahoo.com
  • Received Date: 2020-02-13
    Accepted Date: 2020-03-29
  • In this study, a model for prediction of lignocellulose components of agricultural residues has been developed with Fourier Transformed Near Infrared (FT-NIR) spectroscopy data. Two calibration techniques (Principal Component Regression (PCR) and Partial Least Square Regression (PLSR)) were assessed for prediction of lignin, holocellulose, α-cellulose, pentosan and ash, and found the PLSR better for lignin, holocellulose and α-cellulose. The PCR also produced better results for quantification of pentosan and ash. Spectral range (7000-5000cm-1) showed more informative than other parts of the spectral data. The PLSR showed maximum value of R2 (R2=0.91%) for prediction of holocellulose. For the prediction of pentosan, the PCR was better (R2=0.68%). The PCR also showed better results (R2=86%) for quantification of ash. To determine amount of lignin, the PLSR was the best (R2=0.83%) when the spectral data were de-trained and smoothed with Savitzky-Golay (S-G) filtering simultaneously. For prediction of α-cellulose, the PLSR was the best model (R2=0.94%) when the data were pretreated with mean normalization. Considering the best alternatives inNear Infrared (NIR) data preprocessing and calibration techniques, methods for quantification of lignocellulose components of agricultural residues have been developed which is rapid, cost effective, and less chemical intensive and easily usable in pulp and paper industries and pulp testing laboratories.
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  • [1]

    Akhtaruzzamen, A.F.M., Shafi, M., 1993. Pulping of jute. Tappi J. 78, 106.
    [2]

    Alves, A., Santos, A., da Silva Perez, D., Rodrigues, J., Pereira, H., Simões, R., Schwanninger, M., 2007. NIR PLSR model selection for Kappa number prediction of maritime pine Kraft pulps. Wood Sci. Technol. 41, 491-499.
    [3]

    Colares, C.J., Pastore, T., Coradin, V.T., 2015. Exploratory analysis of the distribution of lignin and cellulose in woods by Raman imaging and chemometrics. J. Braz. Chem. Soc. 26, 1297-1305.
    [4]

    Fahey, L.M., Nieuwoudt, M.K., Harris, P.J., 2019. Predicting the cell-wall compositions of solid Pinusradiata (radiata pine) wood using NIR and ATR FTIR spectroscopies. Cellulose 26, 7695-7716.
    [5]

    Ferdous, T., Quaiyyum, M.A., Bashar, S., Jahan, M.S., 2020. Anatomical, morphological and chemical characteristics of kaun straw (Seetaria-ltalika). Nord. Pulp Pap. Res. J. DOI:10.1515/npprj-2019-0057.
    [6]

    Fiserova, M., Gigac, J., Russ, A., Maholanyiova, M., 2012. Using NIR analysis for determination of hardwood kraft pulp properties. Wood Research, 57, 121-130.
    [7]

    Hattori, T., Murakami, S., Mai, M.K., Yamada, T., Hirochika, H., Ike, M., Tokuyasu, K., Suzuki, S., Sakamoto, M., Umezawa, T., 2012. Rapid analysis of transgenic rice straw using near-infrared spectroscopy. Plant Biotechnol. 29, 359-366.
    [8]

    He, L., Xin, L.P., Chai, X.S., Li, J., 2015. A novel method for rapid determination of alpha-cellulose content in dissolving pulps by visible spectroscopy. Cellulose 22, 2149-2156.
    [9]

    Huang, C., Han, L., Liu, X., Ma, L., 2010. The rapid estimation of cellulose, hemicellulose, and lignin contents in rice straw by near infrared spectroscopy. Energy Sources Part A:Recover. Util. Environ. Eff. 33, 114-120.
    [10]

    Hussain, M.A., Huq, M.E., Rahman, S.M., Ahmed, Z., 2002. Estimation of lignin in jute by titration method. Pak. J. Biol. Sci. 5, 521-522.
    [11]

    Jahan, M.S., 2009. Studies on the effect of prehydrolysis and amine in cooking liquor on producing dissolving pulp from jute (Corchoruscapsularis). Wood Sci. Technol. 43, 213-224.
    [12]

    Jahan, M.S., Chowdhury, D.A.N., Islam, M.K., 2005. Alkalinesulphite anthraquinone methanol (ASAM) pulpig of jute. IPPTA J. 17, 37-43.
    [13]

    Jahan, M.S., Uddin, M.N., Akhtaruzzaman, A.F.M., 2016. An approach for the use of agricultural by-products through a biorefinery in Bangladesh. For. Chron. 92, 447-452.
    [14]

    Kothiyal, V., Jaideep, Bhandari, S., Ginwal, H.S., Gupta, S., 2015. Multi-species NIR calibration for estimating holocellulose in plantation timber. Wood Sci. Technol. 49, 769-793.
    [15]

    Li, X.L., Sun, C.J., Zhou, B.X., He, Y., 2015. Determination of hemicellulose, cellulose and lignin in moso bamboo by near infrared spectroscopy. Sci. Rep. 5, 17210.
    [16]

    Nuruddin, M., Chowdhury, A., Haque, S., Quaiyyum, M.A., 2011.Extraction and characterization of cellulose microfibrils from agricultural wastes in an integrated biorefinery initiative. Cellulose Chemistry and Technology. 45, 347-354.
    [17]

    Poke, F.S., Raymond, C.A., 2006. Predicting extractives, lignin, and cellulose contents using near infrared spectroscopy on solid wood in Eucalyptusglobulus. J. Wood Chem. Technol. 26, 187-199.
    [18]

    Rajesh, K., Ray, A.K., 2006. Artificial neural network for solving paper industry problems:a review. Journal of Scientific & Industrial Research. 65, 565-573.
    [19]

    Roy, T.K., Mohindru, V.K., Behera, N.C., 1998. Jute for specialty pulp. IPPTA J. 10, 81-86.
    [20]

    Saijonkari-Pahkala, K., 2001. Non-wood plants as raw materials for pulp and paper. Agricultural and Food Science in Finland. 10, 1-97.
    [21]

    Santos, A.J.A., Anjos, O., Pereira, H., 2016. Prediction of blackwood Kraft pulps yields with wood NIR-PLSR models. Wood Sci. Technol. 50, 1307-1322.
    [22]

    Silva, J.C., Nielsen, B.H., Rodrigues, J., Pereira, H., Wellendorf, H., 1999. Rapid determination of the lignin content in sitka spruce (Piceasitchensis (bong.) carr.) wood by Fourier transform infrared spectrometry. Holzforschung 53, 597-602.
    [23]

    Uddin, M.N., Ahmed, S., Ray, S., Islam, M.S., Quadery, A.H., 2019. Method for predicting lignocellulose components in jute by transformed FT-NIR spectroscopic data and chemometrics. Nordic Pulp & Paper Research Journal 34, 1-9.
    [24]

    Uddin, M.N., Ray, S.K., Islam, M.S., Nayeem, J., Jahan, M.S., 2017. Development of method for rapid prediction of chemical components of dhaincha using FT-NIR spectroscopy and chemometrics. J. Sci. Technol. For. Prod Process. 6, 22-28.
    [25]

    Yeh, T.F., Yamada, T., Capanema, E., Chang, H.M., Chiang, V., Kadla, J.F., 2005. Rapid screening of wood chemical component variations using transmittance near-infrared spectroscopy. J. Agric. Food Chem. 53, 3328-3332.
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Development of Chemometric Model for Characterization of Non-wood by FT-NIR Data

    Corresponding author: Mohammad Nashir Uddin, m2nashir@yahoo.com
  • a BCSIR Laboratories, Dhaka, Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhaka-1205, Bangladesh;
  • b Department of Applied Chemistry and Chemical Engineering, University of Dhaka, Dhaka-1000, Bangladesh

Abstract: In this study, a model for prediction of lignocellulose components of agricultural residues has been developed with Fourier Transformed Near Infrared (FT-NIR) spectroscopy data. Two calibration techniques (Principal Component Regression (PCR) and Partial Least Square Regression (PLSR)) were assessed for prediction of lignin, holocellulose, α-cellulose, pentosan and ash, and found the PLSR better for lignin, holocellulose and α-cellulose. The PCR also produced better results for quantification of pentosan and ash. Spectral range (7000-5000cm-1) showed more informative than other parts of the spectral data. The PLSR showed maximum value of R2 (R2=0.91%) for prediction of holocellulose. For the prediction of pentosan, the PCR was better (R2=0.68%). The PCR also showed better results (R2=86%) for quantification of ash. To determine amount of lignin, the PLSR was the best (R2=0.83%) when the spectral data were de-trained and smoothed with Savitzky-Golay (S-G) filtering simultaneously. For prediction of α-cellulose, the PLSR was the best model (R2=0.94%) when the data were pretreated with mean normalization. Considering the best alternatives inNear Infrared (NIR) data preprocessing and calibration techniques, methods for quantification of lignocellulose components of agricultural residues have been developed which is rapid, cost effective, and less chemical intensive and easily usable in pulp and paper industries and pulp testing laboratories.

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