欢迎访问《林产化学与工业》，
林产化学与工业

林产化学与工业 ›› 2016, Vol. 36 ›› Issue (6): 63-70.doi: 10.3969/j.issn.0253-2417.2016.06.010

• 研究报告 • 上一篇    下一篇

四种算法用于近红外测定制浆材材性的对比研究

吴珽1, 房桂干1,2, 梁龙1, 林艳1, 熊智新2,3   

  1. 1. 中国林业科学研究院 林产化学工业研究所, 生物质化学利用国家工程实验室, 国家林业局 林产化学工程重点开放性实验室, 江苏省生物质能源与材料重点实验室, 江苏 南京 210042;
    2. 南京林业大学 林业资源高效加工利用协同创新中心, 江苏 南京 210037;
    3. 南京林业大学 轻工科学与工程学院, 江苏 南京 210037
  • 收稿日期:2016-05-12 出版日期:2016-12-25 发布日期:2016-12-23
  • 通讯作者: 房桂干,研究员,博士,博士生导师,研究领域:制浆造纸清洁生产、环保和生物质利用研究;E-mail:fangguigan@icifp.cn E-mail:fangguigan@icifp.cn
  • 作者简介:吴珽(1988-),男,江苏兴化人,博士生,主要从事制浆造纸原料在线检测及工艺优化
  • 基金资助:
    国家林业局948技术引进项目（2014-4-31）；江苏省生物质能源与材料重点实验室项目基金（JSBEM-S-201510）；江苏省自然科学基金（BK20160151）

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

PDF (PC)

644

摘要： 采集了常见制浆材（桉木、相思木及杨木）样品的近红外光谱，测定了样品的基本密度、综纤维素、木质素和苯醇抽出物含量，用人为控制水分的方法测定了样品的水分含量。对原始光谱进行预处理后，分别运用偏最小二乘法（PLS）、LASSO算法、支持向量机法（SVR）和人工神经网络法（BP-ANN）建立基本密度、水分含量、综纤维素、木质素和苯醇抽出物含量的预测模型。对预测模型进行独立验证，结果显示：LASSO算法建立的基本密度和综纤维素模型性能最优，其预测均方根误差（RMSEP）分别为0.006 3 g/cm3和0.49%，绝对偏差（AD）范围分别为-0.008 8～0.009 6 g/cm3和-0.85%~0.87%；PLS建立的水分含量模型及苯醇抽出物模型最优，RMSEP值分别为1.21%和0.24%，AD范围分别为-1.99%～2.03%和-0.35%～0.38%；SVR建立的木质素模型最优，RMSEP值为0.43%，AD范围为-0.76%～0.74%，均满足制浆造纸工业中对误差的要求。

关键词: 近红外光谱, 制浆材, 材性, 算法

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

中图分类号: 

版权所有 © 中国林业科学研究院林产化学工业研究所　苏ICP备08107184号-3