|
|
|
|
|
|
| Near Infrared Spectroscopy Modeling Method of Wood Tensile Strength Based on MC-UVE-IVSO |
| JIANG Da-peng2, GAO Li-bin2, CHEN Jin-hao2, ZHANG Yi-zhuo1* |
1. College of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, China
2. College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China
|
|
|
|
|
Abstract The tensile strength is an important index to assess the mechanical properties of the wood. In order to solve the problems of low model accuracy caused by the small samples and redundant wavelength information in near-infrared spectroscopy modeling, a novel method combining wavelength optimization of MC-UVE-IVSO and PLS is proposed to predict the wood tensile strength. Firstly, 150 birch samples were selected as experimental objects, and the near-infrared spectrometer in the band of 900~1 700 nm was used to collect the spectral data of the test specimens, and the true tensile strength values were obtained by the mechanical testing machine. Secondly, the collected spectral data were preprocessed to complete smoothing filtering by combining multivariate scattering correction (MSC), first-order derivation and convolution smoothing (SG). Thirdly, the optimization methods, which include the variable combination cluster analysis algorithm (VCPA), the Monte Carlo uninformative variable elimination method (MC-UVE), the iterative variable subset optimization algorithm (IVSO) and the MC-UVE-IVSO combined optimization algorithm, were applied to select spectral wavelength features, and the optimal wavelength results based on different method were compared. Finally, the partial least squares birch tensile strength prediction model was established based on the selected wavelength of MC-UVE-IVSO. The experimental results show that the number of spectral variables is reduced from 512 to 98 based on the MC-UVE-IVSO and PLS, and the selected wavelength features account for 19% of the total wavelength. The predicted coefficient of determination (R2) is 0.940 4. The root mean square error of prediction (RMSEP) is 12.370 7. The ratio of performance to deviation (RPD) is 3.162 4, compared with full band, MC-UVE, VCPA,MC-UVE-VCPA and IVSO, R2 indicators (0.926 5, 0.828 2, 0.931 7, 0.934 3), RMSEP indicators (13.910 5, 17.355 2, 13.402 8, 14.070 5) and RPD indicators (2.812 3, 2.254 1, 2.918 8, 2.780 3) have been improved to varying degrees; In addition, the box plot of the prediction model established by statistical characteristic wavelengths further proves the stability of the MC-UVE-IVSO algorithm in dealing with multivariate wavelengths. The experimental results proved that the MC-UVE method could eliminate most of the variables, which are not related to the model, and the IVSO algorithm can effectively search for the optimal subset of variables. Based on the MC-UVE-IVSO optimization algorithm, the combination method has complementary advantages, and the optimized features can improve the accuracy and stability of the birch tensile strength prediction model. The method provides a theoretical basis for Non-destructive testing of wood samples based on near-infrared spectroscopy.
|
|
Received: 2022-05-13
Accepted: 2023-02-09
|
|
|
|
Corresponding Authors:
ZHANG Yi-zhuo
E-mail: nefuzyz@163.com
|
|
[1] Hänsel A, Sandak J, Sandak A, et al. Wood Material Science & Engineering, 2022，17(3)：230.
[2] Kurata Y. Forests, 2020, 11(6): 644.
[3] FU Rui-yun, FU Xiao-hui, ZHANG Wen-bo, et al（符瑞云, 符小慧, 张文博，等）. Spectroscopy and Spectral Analysis（光谱学与光谱分析）, 2022, 42(1): 56.
[4] WANG Cheng-kun, ZHAO Peng（王承琨, 赵 鹏）. Journal of Infrared and Millimeter Waves（红外与毫米波学报）, 2020, 39(1): 72.
[5] YUN Yong-huan, DENG Bai-chuan, LIANG Yi-zeng（云永欢, 邓百川, 梁逸曾）. Chinese Journal of Analytical Chemistry(分析化学), 2015, 43(11): 1638.
[6] Pang T, Rao L, Chen X, et al. Journal of Applied Spectroscopy, 2021, 87(6): 1196.
[7] Li Huanhuan, Zhu Jiaji, Jiao Tianhui, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2020, 243: 118765.
[8] WANG Yi-miao, ZHU Jin-lin, ZHANG Hui，et al（王怡淼, 朱金林, 张 慧，等）. Chinese Journal of Luminescence (发光学报), 2018, 39(9): 1310.
[9] Li Ying, Via Brian K, Li Yaoxiang. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2020, 240: 118566.
[10] Wang Weiting, Yun Yonghuan, Deng Baichuan, et al. RSC Advances, 2015, 5(116): 95771.
[11] Wang Yujie, Li Menghui, Li Luqing, et al. Food Chemistry, 2021, 345: 128816.
[12] Wang Yujie, Li Menghui, Li Luqing, et al. Food Chemistry, 2021, 345: 128816.
[13] Wang Caixia, Wang Songlei, He Xiaoguang, et al. Meat Science, 2020, 169: 108194.
[14] Jin Xiaoli, Chen Xiaoling, Shi Chunhai, et al. Bioresource Technology, 2017, 241: 603.
|
| [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] |
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. |
| [11] |
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. |
| [12] |
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. |
| [13] |
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. |
| [14] |
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. |
| [15] |
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. |
|
|
|
|
