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Chemistry and Industry of Forest Products ›› 2016, Vol. 36 ›› Issue (6): 63-70.doi: 10.3969/j.issn.0253-2417.2016.06.010

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Four Kinds of Algorithms Used for the Determination of Pulpwood Properties by Near Infrared Spectroscopy

WU Ting1, FANG Gui-gan1,2, LIANG Long1, LIN Yan1, XIONG Zhi-xin2,3   

  1. 1. Institute of Chemical Industry of Forest Products, CAF, National Engineering Lab. for Biomass Chemical Utilization, Key and Open Lab. of Forest Chemical Engineering, SFA, Key Lab. of Biomass Energy and Material, Jiangsu Province, Nanjing 210042, China;
    2. Collaborative Innovation Center for High Efficient Processing and Utilization of Forestry Resources, Nanjing Forestry University, Nanjing 210037, China;
    3. College of Light Industry Science and Engineering, Nanjing Forestry University, Nanjing 210037, China
  • Received:2016-05-12 Online:2016-12-25 Published:2016-12-23

Abstract: Near infrared(NIR) spectra of pulpwood species were collected. The basic density, holocellulose, lignin and benzene-alcohol extractive content of samples were analyzed by traditional methods. The moisture content under manual control was analyzed,too. After the pretreatment of the original spectra, partial least squares(PLS) algorithm, LASSO algorithm, support vector regression(SVR) algorithm and back propagation artificial neural network(BP-ANN) algorithm were used to build the prediction models for basic density, moisture content, holocellulose, lignin and benzene-alcohol extractive content. The independent verification of the prediction models showed that the optimal model for basic density was built by LASSO algorithm with the root mean square error(RMSEP) of 0.006 3 g/cm3 and the absolute deviation(AD) of -0.008 8-0.009 6 g/cm3. The optimal model for moisture content was built by PLS algorithm with the RMSEP of 1.21% and the AD of -1.99%-2.03%. The optimal model for holocellulose content was built by LASSO algorithm with the RMSEP of 0.49% and the AD of -0.85%-0.87%. The optimal model for lignin content was built by SVR algorithm with the RMSEP of 0.43% and the AD of -0.76%-0.74%. The optimal model for the benzene-alcohol extractive content was built by PLS algorithm with the RMSEP of 0.24% and the AD of -0.35%-0.38%. The prediction performance of the models could meet the needs of pulping and papermaking industry. The detemination accuracy of pulpwood properties were promoted by algorithm selection.

Key words: near infrared spectroscopy, pulpwood, wood properties, algorithm

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