文献类型：期刊文献

英文题名：Prediction of holocellulose and lignin content of pulp wood feedstock using near infrared spectroscopy and variable selection

作者：Liang, Long[1,2,3,4,5] Wei, Lulu[1,2,3,4,5] Fang, Guigan[1,2,3,4,5] Xu, Feng[6] Deng, Yongjun[1,2,3,4,5] Shen, Kuizhong[1,2,3,4,5] Tian, Qingwen[1,2,3,4,5] Wu, Ting[1,2,3,4,5] Zhu, Beiping[1,2,3,4,5]

第一作者：梁龙;Liang, Long

通信作者：Fang, GG[1];Fang, GG[2];Fang, GG[3];Fang, GG[4];Fang, GG[5];Xu, F[6]|[a000589b6171897e30b38]房桂干;

机构：[1]Chinese Acad Forestry, Inst Chem Ind Forest Prod, Nanjing 210042, Jiangsu, Peoples R China;[2]Key Lab Biomass Energy & Mat, Nanjing 210042, Jiangsu, Peoples R China;[3]Coinnovat Ctr Efficient Proc & Utilizat Forest Re, Nanjing 210042, Jiangsu, Peoples R China;[4]Natl Forestry & Grassland Adm, Key Lab Chem Engn Forest Prod, Nanjing 210042, Jiangsu, Peoples R China;[5]Natl Engn Lab Biomass Chem Utilizat, Nanjing 210042, Jiangsu, Peoples R China;[6]Beijing Forestry Univ, Beijing Key Lab Lignocellulos Chem, Beijing 100083, Peoples R China

年份：2020

卷号：225

外文期刊名：SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY

收录：;EI(收录号：20193807443606);Scopus(收录号：2-s2.0-85072223044);WOS:【SCI-EXPANDED(收录号:WOS:000504048200039)】；

基金：This work was financially supported by the National Key Research and Development Program of China: High Efficiency Clean Pulping and Functional Product Production Technology Research (Grant Number: 2017YFD0601005).

语种：英文

外文关键词：Near infrared spectroscopy; Variable selection; Pulp wood feedstock; Holocellulose; Lignin

摘要：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 nondestructive analysis technique, near infrared (NIR) spectroscopy provides an alternative for wood properties online 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. (C) 2019 Elsevier B.V. All rights reserved.