文献类型：期刊文献

中文题名：毛竹化学成分光谱分析的快速建模方法研究

英文题名：Rapid Modeling Method for Spectroscopic Analysis of Chemical Components of Bamboo

作者：李改云[1] 黄安民[1] 秦特夫[1]

第一作者：李改云

通信作者：Qin, TF[1]

机构：[1]中国林业科学研究院木材工业研究所

年份：2009

期号：7

起止页码：1868-1871

中文期刊名：光谱学与光谱分析

外文期刊名：Spectroscopy and Spectral Analysis

收录：CSTPCD;;EI(收录号：20092912195594);Scopus(收录号：2-s2.0-67650096482);WOS:【SCI-EXPANDED(收录号:WOS:000267632100034)】；北大核心:【北大核心2008】；CSCD:【CSCD2011_2012】；PubMed;

基金：国家"十一五"科技攻关项目(2006BAD18B10);中央级公益性科研院所基金项目(CAFINT2007C04)资助

语种：中文

中文关键词：近红外光谱;综纤维素;木质素;混合样品;建模方法

外文关键词：Near infrared spectra; Holocellulose; Lignin; Mixed sample; Modeling method

分类号：S795.9

摘要：研究了毛竹综纤维素和木质素含量近红外光谱定量分析方法的建立。选用不同竹龄、纵向和横向部位的54个竹材粉末样品,用湿化学方法测定其综纤维素和木质素含量。在综纤维素和木质素含量的分布范围内,从低值、中间值和高值中挑选11个代表性样品,11个样品按预定比例混合得到21个混合样品,混合样品的综纤维素和木质素含量计算得到,再挑选22个目标成分含量不同的样品组成54个样品的校正集。用偏最小二乘法分别建立样品综纤维素含量、木质素含量和近红外漫发射光谱之间的相关模型。结果表明,综纤维素含量的预测模型的相关系数(Rp)为0.92,标准偏差(SEP)为1.04%;木质素含量的预测模型的Rp为0.93,SEP为0.91%,与常规方法建立的模型预测精度相似。说明利用样品混合的方式可快速增加校正集样品的数量、改善校正集样品的分布状况,继而建立稳定可靠的近红外定量分析模型。
A rapid modeling method for predicting the chemical components contents of bamboo was presented. The holocellulose contents and lignin contents of 54 samples from three growth years, two longitudinal positions and three radial positions were analyzed according to traditional chemical methods. Eleven samples were selected based on their holocellulose content and lignin content from these 54 samples to cover the range of holocellulose content and lignin content. Eleven samples were mixed at preset ratio with each other to give 21 mixed samples, the holocellulose content and lignin contents of which were computed. Another 22 samples with different chemical component contents were selected from the same 54 samples. The relationship between the chemical component contents and the diffuse reflectance NIR spectra of these samples was established using partial least squares regression. The correlation coefficient of prediction model for holocellulose content and lignin content was 0.92 and 0. 93, respectively. The standard error of prediction for holocellulose content and lignin content was 1.04% and 0.91%, respectively. The prediction results were similar to those from the prediction models developed by traditional methods. The results presented in this study demonstrate that samples can be prepared rapidly by the mixture of samples with each other and their chemical component contents can be computed. The technique will significantly reduce sampling time and analyzing time without adversely affecting the quality of the model.