近红外技术的广西速生桉抽出物含量测定与模型优化  

Analysis of Extractives Content of Guangxi Fast-Growing Eucalyptus and Models Optimization Based on Near-Infrared Technique

作　　者：朱华 吴珽[2,3] 房桂干 梁龙[2,3] 朱北平[2,3] 佘光辉 ZHU Hua;WU Ting;FANG Gui-gan;LIANG Long;ZHU Bei-ping;SHE Guang-hui(College of Forestry,Nanjing Forestry University,Nanjing 210037,China;Collaborative Innovation Center for High Efficient Processing and Utilization of Forestry Resources,Nanjing Forestry University,Nanjing 210037,China;Institute of Chemical Industry of Forest Products,Chinese Academy of Forestry,Nanjing 210042,China)

机构地区：[1]南京林业大学林学院,江苏南京210037 [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]中国林业科学研究院林产化学工业研究所,江苏南京210042

出　　处：《光谱学与光谱分析》2020年第3期793-798,共6页Spectroscopy and Spectral Analysis

基　　金：国家重点研发计划项目(2017YFD0601005);国家自然科学基金重大项目(31890774)资助

摘　　要：为解决速生桉抽出物测定方法繁琐耗时,木浆生产能耗居高不下等问题,以引种的3种广西速生尾巨桉原料(DH32-29,DH32-26,DH33-27)为研究对象,采集了144个样本的近红外光谱,按国标方法测定全部样品的苯醇抽出物和1%NaOH抽出物含量。在Matlab 8.0中采用信号平滑,一阶、二阶导数,矢量归一化,多元散射校正等方法预处理原始光谱,用偏最小二乘法、支持向量机法和人工神经网络法以及常用于宏观经济分析的LASSO法分别结合上述预处理方法建立模型,筛选出最优建模方法。运用遗传算法对波段进行选择,提高了模型的精确度从而优化了模型。确定了建立苯醇抽出物含量模型时,可联用平滑、MSC和一阶导数预处理光谱数据,以1345.0~1821.4和2127.8~2241.3 nm区间波段参与建模,建模方法为偏最小二乘法,最佳主成分数为9时,模型有最好的精确度。其RMSEP值可达0.25%,绝对偏差范围为-0.39%~0.38%。筛选出的波段包含了如1410和1447 nm附近酚羟基伸缩振动的一级倍频,2133 nm处苯环上碳氢键的伸缩振动与碳碳双键伸缩振动的合频等苯醇抽出物的特征波段。建立1%NaOH抽出物分析模型时,可联用平滑、矢量归一化和一阶导数预处理,选择1138.2~2363.0 nm波段数据,建模方法为LASSO,选取的调整参数值μ为12.61,此时模型精确度最高。RMSEP值为0.37%,绝对偏差范围为-0.56%~0.53%。筛选出的波段包含了1158和1170 nm附近乙酰脂基团CH3中C-H的伸缩振动二级倍频吸收,1666,1681和1790 nm附近CH3中C-H伸缩振动的一级倍频吸收等特征吸收。模型的预测能力从组分结构角度得到了解释。模型的RPD值分别为4.67和5.77,模型性能均可满足实际需求,有望应用于制浆造纸生产线上的速生桉抽出物含量分析。研究结果表明,通过预处理方法选择和建模方法选择,结合遗传算法的应用,可以建立并优化广西速生桉木抽出物含量的近红外测定模�In pulping and papermaking industry,extractives of wood chips influence the impregnation efficiency,pulp energy consumption and pulp yield.But traditional analysis methods for the content of extractives are not applicable for industrial online monitoring because of being time consuming and costly.Therefore,the present study used near infrared(NIR)spectroscopy to predict rapidly extractives content of three species of fast-growing Eucalyptus urophylla×E.grandi chips(DH32-29,DH32-26,DH33-27)grown in China’s Guangxi Province.NIR spectra of 144 fast-growing Eucalyptus were collected using a holographic grating spectrometer equipped with a halogen illumination and array detector.The benzene-alcohol extractives and 1%NaOH extractives content of 144 samples were gravimetrically determined according to the Chinese national standard test method respectively.The near-infrared spectrum were pretreated using smoothing,first derivative,second derivative,vector normalization and multivariate scattering correction in Matlab 8.0,and the models were developed for various pretreatment methods by loading PLS,LASSO,SVR and ANN algorithm.The optimal modeling methods were selected.Genetic algorithm was used to select the bands,which improved the accuracy of the models and optimized the models.In conclusion,in order to develop analysis model of benzene-alcohol extractives,smoothing,MSC and first derivative methods should be used to preprocess the original spectrum,the bands of 1345.0~1821.4 and 2127.8~2241.3 nm were selected,meanwhile,the partial least squares algorithm was used with the optimal factor 9.The model had the best accuracy for the RMSEP value as low as 0.25%,and the absolute deviation range was-0.39%~0.38%.The optimal bands between 1345.0~1821.4 and 2127.8~2241.3 nm have been associated with O-H stretching(1 st overtone)of phenolic compound(1410 and 1447 nm),as well as C-H stretching and■stretching group frequencies of benzene ring(2133 nm)and other characteristic absorption.In order to establish the content analysis

关 键 词：近红外技术 LASSO算法 预处理 遗传算法 

分 类 号：O433[机械工程—光学工程]