|
|
|
|
|
|
Prediction of Basic Density of Wood Chips Using Near-Infrared
Spectroscopy and Moisture Content Correction Algorithm |
LIANG Long1, 2, 3, 4, WU Ting1, 4*, SHEN Kui-zhong1, XIONG Zhi-xin5, XU Feng2*, FANG Gui-gan1* |
1. 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 Research Center of Low-Carbon Processing and Utilization of Forest Biomass, Nanjing 210042, China
2. College of Materials Science and Technology, Beijing Forestry University,Beijing Key Laboratory of Lignocellulosic Chemistry, Beijing 100083, China
3. Institute of Ecological Conservation and Restoration, Chinese Academy of Forestry, Beijing 100091, China
4. Key Laboratory of Chemistry and Engineering of Forest Products, State Ethnic Affairs Commission,Guangxi Key Laboratory of Chemistry and Engineering of Forest Products, Guangxi University for Nationalities, Nanning 530006, China
5. College of Light Industry and Food Engineering, Nanjing Forestry University, Nanjing 210037, China
|
|
|
Abstract Wood basic density is an important indicator for assessing the pulping properties of raw wood materials. Rapidly determining the basic density of wood chips using near-infrared spectroscopy (NIRS) can provide basic theoretical data for developing and optimising pulp production processes. However, the source complicacy of raw material leads to high variability within the moisture content of wood chips. These fluctuations in the raw material make it difficult for the NIRS model to give a stable prediction performance. In this paper, the moisture desorption process of poplar chips was dynamically monitored by near-infrared spectroscopy. Principal component analysis (PCA) was applied to distinguish the spectral features due to moisture content to explore the change of free water and bound water in wood fiber. In order to investigate the effect of moisture content on the NIRS prediction of wood density, the partial least square calibration (PLS) models were built using wood chips with different moisture content conditions, respectively. And then external parameter orthogonalization algorithm (EPO) was used to improve the robustness of predictive models by eliminating the influence of chip moisture. The results showed that the best prediction accuracy was obtained from water-saturated chips spectra due to full access to information about fiber structures. However, much water absorption information in the spectra was redundant and useless for modeling, and the variations in moisture content also led to unstable prediction performance. The spectral moisture correction based on EPO was an effective method for desensitizing the calibration model to the influence of moisture content, enabling robust and accurate prediction of basic density. The EPO-PLS model provided a performance with a root mean square error (RMSE) of 12.23 kg·m-3, determination coefficients (R2) of 0.883 4, and residual prediction deviation (RPD) of 2.93 under different moisture content. This study built a robust NIR calibration model which was robustified against the influence of the variations in moisture content on the wood density prediction. This technology may facilitate the expansion of potential applications of NIR spectroscopy in the paper and pulp industry.
|
Received: 2022-04-26
Accepted: 2022-08-16
|
|
Corresponding Authors:
WU Ting, XU Feng, FANG Gui-gan
|
|
[1] SHI Chuan-xi, YU Ying-liang, ZHU Ying-qi, et al(石传喜,于英良,朱莹琦,等). Journal of Forestry Engineering(林业工程学报), 2020, 5(5): 57.
[2] FU Rui-yun, ZHANG Wen-bo, LI Dong-qing, et al(符瑞云,张文博,黎冬青,等). Journal of Forestry Engineering(林业工程学报), 2021, 6(2): 114.
[3] Santos A, Alves A, Simes R, et al. Journal of Near Infrared Spectroscopy, 2012, 20(2): 267.
[4] Alves A, Santos A, Rozenberg P, et al. Wood Science and Technology, 2012, 46: 157.
[5] Li Y, Via K, Li Y. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2020, 240(15): 118566.
[6] Mora C, Schimleck L, Clark A, et al. Journal of Near Infrared Spectroscopy, 2011, 19(5): 391.
[7] YIN Shi-kui, LI Chun-xu, MENG Yong-bin, et al(尹世逵,李春旭,孟永斌,等). Journal of Central South University of Forestry & Technology(中南林业科技大学学报), 2020, 40(5): 171.
[8] Diesel K, Costa F, Pimenta A, et al. Wood Science and Technology, 2014, 48: 949.
[9] Fujimoto T, Kobori H, Tsuchikawa S. Journal of Near Infrared Spectroscopy, 2012, 20(3): 353.
[10] Liang L, FangG, Deng Y, et al. Forest Science, 2019, 65(5): 548.
[11] Hans G, Allison B. Journal of Near Infrared Spectroscopy, 2019, 27(4): 259.
[12] Roger J, Chauchard F, Bellon-Maurel V. Chemometrics and Intelligent Laboratory Systems, 2003, 66(2): 191.
[13] LI Shuo, LI Chun-lian, CHEN Song-chao, et al(李 硕,李春莲,陈颂超,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2021, 41(4): 1234.
[14] Minasny B, McBratney A, Bellon-Maurel V, et al. Geoderma, 2011, 167: 118.
[15] Ma T, Schimleck L, Inagaki T, et al. Holzforschung, 2021, 75(4): 345.
[16] Huang C, Chui Y, Gong M, et al. Journal of Bioresources and Bioproducts, 2020, 5(4): 266.
|
[1] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
[2] |
BAO Hao1, 2,ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
[3] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
[4] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
[5] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
[6] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
[7] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
[8] |
ZHANG Shu-fang1, LEI Lei2, LEI Shun-xin2, TAN Xue-cai1, LIU Shao-gang1, YAN Jun1*. Traceability of Geographical Origin of Jasmine Based on Near
Infrared Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3389-3395. |
[9] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
[10] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
[11] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
[12] |
KANG Ming-yue1, 3, WANG Cheng1, SUN Hong-yan3, LI Zuo-lin2, LUO Bin1*. Research on Internal Quality Detection Method of Cherry Tomatoes Based on Improved WOA-LSSVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3541-3550. |
[13] |
HUANG Hua1, LIU Ya2, KUERBANGULI·Dulikun1, ZENG Fan-lin1, MAYIRAN·Maimaiti1, AWAGULI·Maimaiti1, MAIDINUERHAN·Aizezi1, GUO Jun-xian3*. Ensemble Learning Model Incorporating Fractional Differential and
PIMP-RF Algorithm to Predict Soluble Solids Content of Apples
During Maturing Period[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3059-3066. |
[14] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
[15] |
GUO Ge1, 3, 4, ZHANG Meng-ling3, 4, GONG Zhi-jie3, 4, ZHANG Shi-zhuang3, 4, WANG Xiao-yu2, 5, 6*, ZHOU Zhong-hua1*, YANG Yu2, 5, 6, XIE Guang-hui3, 4. Construction of Biomass Ash Content Model Based on Near-Infrared
Spectroscopy and Complex Sample Set Partitioning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3143-3149. |
|
|
|
|