文献类型：期刊文献

中文题名：基于近红外光谱和水分校正算法的造纸木片基本密度预测

英文题名：Prediction of Basic Density of Wood Chips Using Near-Infrared Spectroscopy and Moisture Content Correction Algorithm

作者：梁龙[1,2,3,4] 吴珽[1,4] 沈葵忠[1] 熊智新[5] 许凤[2] 房桂干[1]

第一作者：梁龙

机构：[1]中国林业科学研究院林产化学工业研究所、江苏省生物质能源与材料重点实验室、江苏省林业资源高效加工利用协同创新中心、国家林业和草原局林产化学工程重点实验室、林木生物质低碳高效利用国家工程研究中心,江苏南京210042;[2]北京林业大学材料科学与技术学院,林木生物质化学北京市重点实验室,北京100083;[3]中国林业科学研究院生态保护与修复研究所,北京100091;[4]广西民族大学林产化学与工程国家民委重点实验室,广西林产化学与工程重点实验室,广西南宁530006;[5]南京林业大学轻工与食品学院,江苏南京210037

年份：2023

卷号：43

期号：8

起止页码：2476-2482

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：20233914773788);Scopus;WOS:【SCI-EXPANDED(收录号:WOS:001054445900021)】；北大核心:【北大核心2020】；CSCD:【CSCD2023_2024】；PubMed;

基金：国家自然科学基金重大项目(31890771);中国林业科学院林业新技术所基本科研业务费专项资金项目(CAFYBB2019SY039);林产化学与工程国家民委重点实验室暨广西林产化学与工程重点实验室开放课题重点项目(GXFK2205)资助。

语种：中文

中文关键词：造纸木片;近红外光谱;基本密度;外部参数正交化算法

外文关键词：Wood chips for the pulp;Near-infrared spectroscopy;Basic density;External parameter orthogonalization algorithm

分类号：O657.3

摘要：基本密度是评估木材制浆造纸性能的重要指标。采用近红外光谱技术检测造纸木片基本密度,实时掌握原料材性变化,能够为制定和优化制浆生产工艺提供基础理论数据。但在实际生产中,原料来源的复杂性造成木片水分含量波动较大,光谱中的水分干扰信息严重影响模型预测效果,成为制约近红外技术实际应用的主要因素。以杨木片为研究对象,通过对木片失水过程的近红外光谱动态监测,结合主成分分析明确光谱中水分吸收信息的特征响应,揭示了木片水分中结合水和自由水的变化规律。分别采用不同水分条件下的木片光谱建立偏最小二乘回归(PLS)模型预测木片基本密度,通过对比分析模型预测性能,探究木片水分变化对近红外预测木片密度的影响,并采用外部参数正交化算法(EPO)消除光谱中水分的干扰,提高模型对水分变化的抗干扰能力。研究结果表明,基于饱水木片光谱的模型具有最好的预测精度,模型的建立主要依靠近红外光谱对木片纤维结构特征信息的获取。而光谱中大量的水分吸收信息对建模是冗余无用的,并且会导致模型对样品水分高度敏感,当测试集水分含量变化时,模型预测出现严重偏差。通过EPO算法对木片失水过程动态光谱的解析,提取水分校正因子,能够有效消除水分变化引起的光谱差异。基于水分校正的基本密度预测模型对不同水分条件下的测试集均表现出稳定的预测性能,均方根误差、决定系数和预测相对标准偏差分别为12.23 kg·m^(-3)、0.8834和2.93。该研究将EPO算法引入对木材材性的近红外光谱分析,构建了抗水分干扰的稳健型基本密度预测模型,较好地解决了水分含量波动对原料材性快速检测的影响,为近红外光谱技术在制浆造纸领域的推广应用提供了依据。
Wood basic density is an important indicator for assessing the pulping properties of raw wood materials.Rapidly determining the basic density of wood chips using near-infrared spectroscopy(NIRS)can provide basic theoretical data for developing and optimising pulp production processes.However,the source complicacy of raw material leads to high variability within the moisture content of wood chips.These fluctuations in the raw material make it difficult for the NIRS model to give a stable prediction performance.In this paper,the moisture desorption process of poplar chips was dynamically monitored by near-infrared spectroscopy.Principal component analysis(PCA)was applied to distinguish the spectral features due to moisture content to explore the change of free water and bound water in wood fiber.In order to investigate the effect of moisture content on the NIRS prediction of wood density,the partial least square calibration(PLS)models were built using wood chips with different moisture content conditions,respectively.And then external parameter orthogonalization algorithm(EPO)was used to improve the robustness of predictive models by eliminating the influence of chip moisture.The results showed that the best prediction accuracy was obtained from water-saturated chips spectra due to full access to information about fiber structures.However,much water absorption information in the spectra was redundant and useless for modeling,and the variations in moisture content also led to unstable prediction performance.The spectral moisture correction based on EPO was an effective method for desensitizing the calibration model to the influence of moisture content,enabling robust and accurate prediction of basic density.The EPO-PLS model provided a performance with a root mean square error(RMSE)of 12.23 kg·m^(-3),determination coefficients(R^(2))of 0.8834,and residual prediction deviation(RPD)of 2.93 under different moisture content.This study built a robust NIR calibration model which was robustified against the influence of the variations in moisture content on the wood density prediction.This technology may facilitate the expansion of potential applications of NIR spectroscopy in the paper and pulp industry.