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近红外光谱分析杨木-桉木混合纸浆原料
引用本文:吴珽,房桂干,梁龙,邓拥军,熊智新.近红外光谱分析杨木-桉木混合纸浆原料[J].光谱学与光谱分析,2018,38(8):2400-2406.
作者姓名:吴珽  房桂干  梁龙  邓拥军  熊智新
作者单位:1. 中国林业科学研究院林产化学工业研究所,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
基金项目:国家重点研发计划项目(2017YFD0601005),国家林业局948项目(2014-4-31)资助
摘    要:近年来,随着林纸一体化战略的推进,多使用混合原料制浆。而混合原料比例及成分含量的快速分析难以实现已成为制约制浆工业发展的瓶颈。为解决此问题,以广泛使用的杨木-桉木混合原料为研究对象,用傅里叶近红外光谱仪采集了131个不同比例的杨木-桉木混合样品和30个单一杨木、桉木样品的近红外光谱;用化学法测定其综纤维素、聚戊糖及Klason木素含量。因主要化学成分含量的近红外光谱信息集中于7 600~4 000 cm-1区间,对该区间的光谱数据进行平滑、标准正态变换和一阶导数的预处理,运用LASSO算法建立了杨木含量与聚戊糖含量模型;对该区间数据进行平滑、标准正态变换和二阶导数预处理后结合LASSO算法建立了综纤维素含量模型;对该区间数据进行平滑、多元信号校正和二阶导数预处理后结合LASSO算法建立了Klason木素含量模型。杨木含量、综纤维素、聚戊糖、Klason 木素含量模型的预测均方根误差分别为1.82%,0.52%,0.67%和0.59%;绝对偏差范围分别为-3.01%~2.94%,-0.91%~0.83%,-0.91%~1.07%,-0.79%~0.92%。4种模型的性能总体上略优于传统偏最小二乘法所建的模型且满足实际需求,可以用于工业生产。

关 键 词:近红外技术  LASSO算法  预处理  混合原料  
收稿时间:2017-08-27

Analysis of Poplar-Eucalyptus Mixed Pulp Raw Materials Based on Near-Infrared Spectroscopy
WU Ting,FANG Gui-gan,LIANG Long,DENG Yong-jun,XIONG Zhi-xin.Analysis of Poplar-Eucalyptus Mixed Pulp Raw Materials Based on Near-Infrared Spectroscopy[J].Spectroscopy and Spectral Analysis,2018,38(8):2400-2406.
Authors:WU Ting  FANG Gui-gan  LIANG Long  DENG Yong-jun  XIONG Zhi-xin
Institution:1. Institute of Chemical Industry of Forest Products, Chinese Academy of Forestry, National Engineering Lab for Biomass Chemical Utilization; 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
Abstract:In recent years, with the advance of forest and paper integration strategy, we often use mixed raw materials pulping. It is difficult to realize the rapid analysis of mixing degree and chemical composition content of raw materials, which has become the bottleneck constraints of pulping industry development. In order to solve this problem, the research chose the widely used poplar-eucalyptus wood mixed raw materials as study object, the near infrared spectrums of 131 poplar-eucalyptus wood samples which poplar content was artificially controlled and 30 single poplar and eucalyptus wood samples were collected with Fourier near infrared spectrometer, then the content of holocellulose, pentosan and Klason lignin was measured by chemical methods. The near-infrared spectra of these major chemical components are concentrated in the 7 600~4 000 cm-1 interval. The model of poplar content and the model of pentosan content were established by LASSO(the least absolute shrinkage and selection operator) algorithm combined with spectral data of 7 600~4 000 cm-1 which was pretreated by smoothing, standard normal variate and first derivative. The holocellulose content model was established with LASSO algorithm combined with same range of spectral data which was pretreated by smoothing, standard normal variate and second derivative. The Klason lignin content model was developed with the same algorithm , the same range of spectral data with the pretreatment of smoothing, multipicative scatter correction and second derivative. Poplar content, holocellulose, pentosan and Klason lignin models have root mean square error of prediction of 1.82%, 0.52%, 0.67% and 0.59% respectively. Absolute deviation (AD) range were -3.01%~2.94%, -0.91%~0.83%, -0.91%~1.07%, -0.79%~0.92%. The models have good performance better than the traditional partial least squares models that can be applied in actual industrial production.
Keywords:Near-infrared spectroscopy technology  LASSO algorithm  Pretreatment  Mixed raw materials  
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