Institute of Chemical Industry of Forest Products, Chinese Academy of Forestry; Key Lab. of Biomass Energy and Material, Jiangsu Province; Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Jiangsu Province; Key Lab. of Chemical Engineering of Forest Products, National Forestry and Grassland Administration; National Engineering Lab. for Biomass Chemical Utilization, Nanjing, 210042, China; Beijing Key Laboratory of Lignocellulosic Chemistry, Beijing Forestry University, Beijing, 100083, China.
Institute of Chemical Industry of Forest Products, Chinese Academy of Forestry; Key Lab. of Biomass Energy and Material, Jiangsu Province; Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Jiangsu Province; Key Lab. of Chemical Engineering of Forest Products, National Forestry and Grassland Administration; National Engineering Lab. for Biomass Chemical Utilization, Nanjing, 210042, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2020 Jan 15;225:117515. doi: 10.1016/j.saa.2019.117515. Epub 2019 Sep 6.
Wood is the main feedstock source for pulp and paper industry. However, chemical composition variations from multispecies and multisource feedstock heavily affect the production continuity and stability. As a rapid and non-destructive analysis technique, near infrared (NIR) spectroscopy provides an alternative for wood properties on-line analysis and feedstock quality control. Herein, near infrared spectroscopy coupled with partial least squares (PLS) regression was used to predict holocellulose and lignin contents of various wood species including poplars, eucalyptus and acacias. In order to obtain more accurate and robust prediction models, a comparison was conducted among several variable selection methods for NIR spectral variables optimization, including competitive adaptive reweighted sampling (CARS), Monte Carlo-uninformative variable elimination (MC-UVE), successive projections algorithm (SPA), and genetic algorithm (GA). The results indicated that CARS method displayed relatively higher efficiency over other methods in elimination of uninformative variables as well as enhancement of the predictive performance of models. CARS-PLS models showed significantly higher robustness and accuracy for each property using lowest variable numbers in cross validation and external validation, demonstrating its applicability and reliability for prediction of multispecies feedstock properties.
木材是制浆造纸工业的主要原料。然而，多树种和多来源原料的化学成分变化严重影响了生产的连续性和稳定性。近红外（NIR）光谱作为一种快速、无损的分析技术，为木材性质的在线分析和原料质量控制提供了一种替代方法。本文采用近红外光谱结合偏最小二乘（PLS）回归法预测了杨树、桉树和金合欢等多种木材的综纤维素和木质素含量。为了获得更准确、更稳健的预测模型，对 NIR 光谱变量优化的几种变量选择方法（竞争自适应重加权采样法（CARS）、蒙特卡罗无信息变量消除法（MC-UVE）、连续投影算法（SPA）和遗传算法（GA））进行了比较。结果表明，CARS 法在消除无信息变量以及提高模型预测性能方面比其他方法更有效。CARS-PLS 模型在交叉验证和外部验证中使用最少的变量数，显示出对每种性质更高的稳健性和准确性，表明其在预测多树种原料性质方面的适用性和可靠性。
