文献类型：期刊文献

英文题名：Prediction of strength properties of poplar alkaline peroxide mechanical pulp using near infrared spectroscopy and multivariate calibration

作者：Liang, Long[1,2] Fang, Guigan[1,2] Wei, Lulu[2] Han, Shanming[2] Deng, Yongjun[2] Zhu, Beiping[2] Wu, Ting[1,2]

第一作者：梁龙

通信作者：Fang, GG[1]|[a000589b6171897e30b38]房桂干;

机构：[1]Chinese Acad Forestry, Res Inst Forestry New Technol, Xiangshan Rd, Beijing 100091, Peoples R China;[2]Chinese Acad Forestry, Natl Engn Lab Biomass Chem Utilizat,Coinnovat Ctr, Inst Chem Ind Forest Prod,Natl Forestry & Grassla, Key Lab Biomass Energy & Mat,Key Lab Chem Engn Fo, Nanjing 210042, Jiangsu, Peoples R China

年份：2020

卷号：109

外文期刊名：VIBRATIONAL SPECTROSCOPY

收录：;EI(收录号：20202808908947);Scopus(收录号：2-s2.0-85087397447);WOS:【SCI-EXPANDED(收录号:WOS:000557907600005)】；

基金：This work was supported by the Fundamental Research Funds of Research Institute of Forest New Technology, Chinese Academy of Forestry (Grant number: CAFYBB2019SY039).

语种：英文

外文关键词：Near infrared spectroscopy; Alkaline peroxide mechanical pulp; Mechanical compressive pre-treatment; Pulp strength properties; Spectral pre-processing

摘要：Application of near infrared (NIR) spectroscopy to pulp and paper research is almost based on chemical pulping, but little research has been directed toward the use of NIR to mechanical pulping. Compared with chemical pulp, mechanical pulp exhibits certain attractive qualities such as high pulping yield, sufficient opacity and bulk. However, because non-cellulose components (hemicelluloses and lignin) are preserved largely in the produced fibre, mechanical pulping process emerges easily production problem, such as inefficient chemical impregnation and unstable pulp properties. Therefore, it is necessary to explore a rapid measurement technique suitable for pulping process control. This study investigated the feasibility of NIR to predict rapidly the strength properties of mechanical pulp. Alkaline peroxide mechanical pulp (APMP), using poplar as raw material, was produced at pilot plant scale, different type of mechanical compressive pre-treatment and different pulping degree were implemented to increase variability in the obtained pulp samples. NIR spectra were collected from the rough and smooth surface of pulp handsheets to develop calibration models for tensile, burst, and tear index. Calibration statistics results indicated that rough surface spectra could provide more accurate and robust calibrations compared with smooth surface spectra. Additionally, spectral signal correction was proved to be a critical step for NIR calibration optimization. Based on spectra collected with multiplicative scatter correction and derivatives pre-processing, excellent NIR prediction performance was obtained with the root mean square error of 1.776 Nm g(-1) for tensile index, 0.108 kPam(2) g-1 for burst index and 0.116 mNm(2) g(-1) for tear index, the RPD values greater than 2.0 satisfying quantitative analysis.