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Near-Infrared Analysis and Models Optimization of Main Components in Pulpwood of Hainan Province |
WU Ting1, 2, 3, LIANG Long1, 3, ZHU Hua3, DENG Yong-jun1, 3, FANG Gui-gan1, 3* |
1. Institute of Chemical Industry of Forest Products, CAF; National Engineering Lab for Biomass Chemical Utilization; Key Lab of Chemical Engineering of Forest Products, National Forestry and Grassland Administration; Key Lab of Biomass Energy and Material, Jiangsu Province, Nanjing 210042, China
2. Gold East Paper (Jiangsu) Co., Ltd., Zhenjiang 212132, China
3. Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Jiangsu Province, Nanjing 210037, China |
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Abstract In order to improve the utilization efficiency of pulpwood in Hainan Province, alleviate the shortage of domestic pulping and papermaking materials, and reduce pollution and overall costs in the pulping and papermaking industry, this study aimed to use near-infrared spectroscopy for the analysis of pulpwood. A holographic grating spectroscopic near-infrared spectrometer with a simple structure and easy modification was used to collect the near-infrared spectrum of 205 samples of pulpwood common in Hainan (E. urophlla× E. tereticornis, Eucalyptus urophylla× grandis, Eucalyptus urophylla, Acacia mangium, Acacia crassicarpa Benth.), and the content of holocellulose and lignin were measured according to the traditional laboratory methods. Suitable pretreatment methods were selected in combination with partial least squares to establish analysis models holocellulose and lignin. Then genetic algorithm was usedto eliminate the irrelevant variables and clarifythe feature absorption of holocellulose and lignin in order to optimize the models. The holocellulose model was established by pretreatment methods of Savitzky-Golay 13 points 3 times smoothing, vector normalization, the first derivative of the original spectrum, with 1 150.3~2 362.0 nm bands participated in modeling. The optimal bands included the characteristic absorption of cellulose such as the 2nd overtone of C—H stretching vibration in CH3 between 1 188~1 196 nm, the 1st overtone of O—H stretching vibration between 1 742~1 633 nm, the group frequencies of formation and stretching vibration of O—H near 2 112 nm. The optimal bands also included the characteristic absorption of pentosan such as the 1st overtone absorption of O—H stretching vibration between 1 470~1 495 nm, and the 2nd overtone absorption of C═O stretching vibration around 1 906 and 1 911 nm. The RMSEP value of the model was 0.55%, and the absolute deviation range was -0.91%~0.87%. The lignin model was established by pretreatment methods of Savitzky-Golay 13 points 3 times smoothing, MSC, the second derivative of the original spectrum, with 1 137.6~1 872.5 and 2 131.0~2 424.1 nm bands participated in modeling. The optimal bands included the characteristic absorption of lignin such as the 2nd overtone of the C—H stretching vibration in the benzene ring and in the CH3 near 1 143 nm, the 1st overtone of the C—H stretching vibration in the benzene ring between 1 670~1 684 nm, the group frequencies of stretching vibration of C—H and C═O near 2 205 nm. The RMSEP value of the model was 0.45%, and the absolute deviation range was -0.76%~0.79%. The two models’ RPD values were 4.71 and 3.47, respectively, which can meet the actual needs of online quick analysis and measurement of the main components of pulpwood. At the same time, this study provides a theoretical basis for the establishment of a near-infrared characterization system for pulpwood, and has a significant significance for the near-infrared technology to help the pulping and papermaking industry to change from automation to intelligence.
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Received: 2020-04-29
Accepted: 2020-08-05
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Corresponding Authors:
FANG Gui-gan
E-mail: ppfangguigan@163.com
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[1] ZHAO Wei(赵 伟). China Pulp & Paper Industry(中华纸业), 2020,(1): 16.
[2] SHEN Kui-zhong, CHEN Yuan-hang, FANG Gui-gan, et al(沈葵忠, 陈远航, 房桂干,等). China Pulp & Paper Industry(中华纸业), 2019, 21: 54.
[3] Liang L, Fang G, Deng Y, et al. Forest Science, 2019, 65: 548.
[4] LI Shui-fang, LI Yi-fan, FU Hong-jun, et al(李水芳, 李一帆, 付红军, 等). Journal of Forestry Engineering(林业工程学报), 2017, 2(6): 45.
[5] LIU Yao-yao, XIONG Zhi-xin, WANG Yong, et al(刘耀瑶, 熊智新, 王 勇, 等). Journal of Forestry Engineering(林业工程学报), 2019, 4(4): 93.
[6] Sun X, Hou Q, Shi H, et al. BioResources, 2018, 13: 5408.
[7] Tong P, Du Y, Zheng K, et al. Chemometrics and Intelligent Laboratory Systems, 2015, 143: 41.
[8] Ishizuka S, Sakai Y, Tanaka-Oda A, et al. Journal of Forest Research, 2014, 19(1): 236.
[9] Hein P R G, Campos A C M, Mendes R F, et al. European Journal of Wood & Wood Products, 2011, 69(3): 436.
[10] Liang L, Wei L, Fang G, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2020, 225: 117515.
[11] Dwivedi P, Vivekanand V, Pareek N, et al. Applied Biochemistry & Biotechnology, 2010, 160(1): 255. |
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