文献类型：期刊文献

英文题名：Analysis of Mixed Pulping Raw Materials of Eucalyptus globulus and Acacia mangium by Near Infrared Spectroscopy Technique Combined with LASSO Algorithm

作者：Wu, Ting[1] Fang, Guigan[1,2] Liang, Long[1] Deng, Yongjun[1] Lin, Yan[1] Xiong, Zhixin[1,2]

第一作者：吴珽

通信作者：Fang, GG[1];Fang, GG[2]

机构：[1]Chinese Acad Forestry, Inst Chem Ind Forest Prod, Nanjing 210042, Jiangsu, Peoples R China;[2]Nanjing Forestry Univ, Collaborat Innovat Ctr High Efficient Proc & Util, Nanjing 210037, Jiangsu, Peoples R China; Nanjing Forestry Univ, Coll Light Ind Sci & Engn, Nanjing 210037, Jiangsu, Peoples R China

年份：2018

卷号：13

期号：1

起止页码：1348-1359

外文期刊名：BIORESOURCES

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000427790000098)】；

基金：The authors are grateful for the support of the Natural Science Foundation of Jiangsu Province of China (Grants: BK20160151), the Research Grant of Jiangsu Province Biomass Energy and Materials Laboratory (JSBEM-S-201510) and the National Key Research and Development Program: 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; LASSO algorithm; Real-time analysis; Pulpwood quality; Chemical composition

摘要：To meet the current demand in China for Eucalyptus globulus and Acacia mangium mixed pulping, a study was conducted to collect the near infrared (NIR) spectra of 150 mixed samples of E. globulus and A. mangium in which the content of E. globulus was manually controlled. After the original spectra were pretreated by first derivative and standard normal variate (SNV), the least absolute shrinkage and selection operator (LASSO) algorithm and cross-validation were used to calculate the optimal adjustment parameters of 14.30, 19.16, 12.10, and 9.74, respectively. The optimal calibration models for the content of E. globulus, holocellulose, pentosan, and acid insoluble lignin were generated. An independent verification of the calibration models showed that the root mean square error of prediction (RMSEP) for these models was 1.59%, 0.54%, 0.66%, and 0.40%, respectively. The absolute deviation (AD) was -2.58% to 2.73%, -0.91% to 0.84%, -1.19% to 1.06%, and -0.61% to 0.64%, respectively. The prediction performance of the four models was sufficient for real-time analysis in the pulping production line. The LASSO algorithm was judged to be efficient for the prediction and analysis of mixed raw materials in pulping industry.