|
|
|
|
|
|
Bark Content Determination of Ultra-Thin Fibreboard by
Hyperspectral Technique |
YANG Chun-mei1, ZHU Zan-bin1, 2*, LI Yu-cheng1, MA Yan1, SONG Hai-yang3 |
1. College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China
2. Xinyang Agriculture and Forestry University, Xinyang 464000, China
3. Asia Union Machinery Co., Ltd., Dunhua 133700, China
|
|
|
Abstract Ultra-thin fiberboard with the thickness of 0.8mm is an innovative experimental product in the fiberboard category. The bark content greatly influences the setting of its production equipment parameters and the quality indicators such as static curvature strength and water resistance. It is important to determine the bark content in ultra-thin fiberboard wood fiber accurately. At present, the accurate determination of bark content is difficult. A fiber bark content detection model was established by hyperspectral near-infrared imaging system combined with relevant algorithms, and the fiber bark content detection method was innovated. In this experiment, spectroscopic sample images of poplar fibers containing poplar bark of 0%, 3%, 5%, 7%, 10%, 12%, 15%, 20%, 25%, 30%, and 100% were determined by the hyperspectral imager. The results of pretreatment of mean centralization (MC), multiple scattering corrections (MSC), standard normal variable transformation (SNV) and first-order (1-Der) derivative were analyzed, then the MSC was selected as the best pretreatment method for this test model. The spectral data pretreated by MSC were extracted by SPA and CARS, and the band combination with the highest correlation with the bark content was obtained, and the full-band model was compared and analyzed to establish a partial least squares regression (PLSR) model. From the experimental data, we can see differences in the predictive performance of the model of partial minimum secondary return (PLSR) established by pretreatment of MC, MSC, SNV and 1-Der. Among them, the performance of the MSC-PLSR model is the best. The correction determinant R2C is 0.994, the prediction determinant R2P is 0.985, the correction square root error RMSEC is 0.831%, and the prediction square root error RMSEP is 1.336%. 37 and 49 characteristic bands were extracted by SPA and CARS respectively, among which the CARS model was better, R2C was 0.991, R2P was 0.979, RMSEC was 0.885%, and RMSEP was 1.335%. The experimental results show that the hyperspectral imaging system combined with the corresponding algorithm can realize the detection of the bark content of the fiber. The study's results provide technical support and theoretical reference for the detection of the bark content in the production of ultra-thin fiberboard, which can effectively realize the quantitative detection of the bark content in the fiber, and innovate a model method that can determine the bark content of the fiberboard.
|
Received: 2022-06-01
Accepted: 2022-09-12
|
|
Corresponding Authors:
ZHU Zan-bin
E-mail: zlx20063500@126.com
|
|
[1] Gruber L, Seidl L, Zanetti M, et al. Forests, 2021, 12(11): 1480.
[2] Nosek R, Holubcik M, Jandacka J. Bioresources, 2016, 11(1): 44.
[3] Eich S, Volk T A. Eisenbies, M. H. BioEnergy Research, 2015, 8(4): 1661.
[4] JIA Na, LIU Bing, HUA Jun, et al(贾 娜, 刘 冰, 花 军, 等). China Wood Industry(木材工业), 2015, 29(3): 35.
[5] BAI Zong-xiu, ZHU Rong-guang, WANG Shi-chang, et al(白宗秀, 朱荣光, 王世昌, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2021, 37(17): 276.
[6] Ma T, Inagaki T, Tsuchikawa S. Journal of Near Infrared Spectroscopy, 2018, 26(6): 398.
[7] Fujimoto T, Kobori H, Tsuchikawa S. Journal of Near Infrared Spectroscopy, 2012, 20(3): 353.
[8] Fernandes A, Lousada J, Morais J,et al. Holzforschung, 2013, 67(1): 59.
[9] Reis C A, Cisneros A B, Lopes da Silva E A, et al. International Wood Products Journal, 2019, 10(4): 168.
[10] Stefansson P, Thiis T, Gobakken L R, et al. Wood Material Science and Engineering, 2021, 16(1): 49.
[11] WU Jing-zhu, ZHANG Le, LI Jiang-bo, etal(吴静珠, 张 乐, 李江波, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2022, 53(5): 302.
[12] ZHANG Wen-zhi(张文治). China Wood-Based Panels(中国人造板), 2022, 29(2): 8.
[13] ZHU Yu-hui, WEN Liang, ZHANG Yao-li, et al(朱玉慧, 闻 靓, 张耀丽, 等). Journal of Forestry Engineering(林业工程学报), 2020, 5(3): 54.
[14] TANG Hai-tao, MENG Xiang-tian, SU Xun-xin, et al(唐海涛, 孟祥添, 苏循新, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2021, 37(2): 105.
[15] ZHANG Ting-ting, ZHAO Bin, YANG Li-ming, et al(张婷婷, 赵 宾, 杨丽明, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2019, 39(8): 2608.
|
[1] |
WANG Cai-ling1,ZHANG Jing1,WANG Hong-wei2*, SONG Xiao-nan1, JI Tong3. A Hyperspectral Image Classification Model Based on Band Clustering and Multi-Scale Structure Feature Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 258-265. |
[2] |
GAO Hong-sheng1, GUO Zhi-qiang1*, ZENG Yun-liu2, DING Gang2, WANG Xiao-yao2, LI Li3. Early Classification and Detection of Kiwifruit Soft Rot Based on
Hyperspectral Image Band Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 241-249. |
[3] |
WU Hu-lin1, DENG Xian-ming1*, ZHANG Tian-cai1, LI Zhong-sheng1, CEN Yi2, WANG Jia-hui1, XIONG Jie1, CHEN Zhi-hua1, LIN Mu-chun1. A Revised Target Detection Algorithm Based on Feature Separation Model of Target and Background for Hyperspectral Imagery[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 283-291. |
[4] |
CHU Bing-quan1, 2, LI Cheng-feng1, DING Li3, GUO Zheng-yan1, WANG Shi-yu1, SUN Wei-jie1, JIN Wei-yi1, HE Yong2*. Nondestructive and Rapid Determination of Carbohydrate and Protein in T. obliquus Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3732-3741. |
[5] |
LI Wei1, TAN Feng2*, ZHANG Wei1, GAO Lu-si3, LI Jin-shan4. Application of Improved Random Frog Algorithm in Fast Identification of Soybean Varieties[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3763-3769. |
[6] |
HUANG You-ju1, TIAN Yi-chao2, 3*, ZHANG Qiang2, TAO Jin2, ZHANG Ya-li2, YANG Yong-wei2, LIN Jun-liang2. Estimation of Aboveground Biomass of Mangroves in Maowei Sea of Beibu Gulf Based on ZY-1-02D Satellite Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3906-3915. |
[7] |
ZHOU Bei-bei1, LI Heng-kai1*, LONG Bei-ping2. Variation Analysis of Spectral Characteristics of Reclaimed Vegetation in an Ionic Rare Earth Mining Area[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3946-3954. |
[8] |
YUAN Wei-dong1, 2, JU Hao2, JIANG Hong-zhe1, 2, LI Xing-peng2, ZHOU Hong-ping1, 2*, SUN Meng-meng1, 2. Classification of Different Maturity Stages of Camellia Oleifera Fruit
Using Hyperspectral Imaging Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3419-3426. |
[9] |
FU Gen-shen1, LÜ Hai-yan1, YAN Li-peng1, HUANG Qing-feng1, CHENG Hai-feng2, WANG Xin-wen3, QIAN Wen-qi1, GAO Xiang4, TANG Xue-hai1*. A C/N Ratio Estimation Model of Camellia Oleifera Leaves Based on
Canopy Hyperspectral Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3404-3411. |
[10] |
SHEN Ying, WU Pan, HUANG Feng*, GUO Cui-xia. Identification of Species and Concentration Measurement of Microalgae Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3629-3636. |
[11] |
XIE Peng, WANG Zheng-hai*, XIAO Bei, CAO Hai-ling, HUANG Yi, SU Wen-lin. Hyperspectral Quantitative Inversion of Soil Selenium Content Based on sCARS-PSO-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3599-3606. |
[12] |
QIAN Rui1, XU Wei-heng2, 3 , 4*, HUANG Shao-dong2, WANG Lei-guang2, 3, 4, LU Ning2, OU Guang-long1. Tea Plantations Extraction Based on GF-5 Hyperspectral Remote Sensing
Imagery in the Mountainous Area[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3591-3598. |
[13] |
ZHU Zhi-cheng1, WU Yong-feng2*, MA Jun-cheng2, JI Lin2, LIU Bin-hui3*, JIN Hai-liang1*. Response of Winter Wheat Canopy Spectra to Chlorophyll Changes Under Water Stress Based on Unmanned Aerial Vehicle Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3524-3534. |
[14] |
YANG Lei1, 2, 3, ZHOU Jin-song1, 2, 3, JING Juan-juan1, 2, 3, NIE Bo-yang1, 3*. Non-Uniformity Correction Method for Splicing Hyperspectral Imager Based on Overlapping Field of View[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3582-3590. |
[15] |
SUN Lin1, BI Wei-hong1, LIU Tong1, WU Jia-qing1, ZHANG Bao-jun1, FU Guang-wei1, JIN Wa1, WANG Bing2, FU Xing-hu1*. Identification Algorithm of Green Algae Using Airborne Hyperspectral and Machine Learning Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3637-3643. |
|
|
|
|