|
|
|
|
|
|
Classification of Terahertz Rosewood Based on Continuous Projection Algorithm and Random Forest |
WANG Yuan1, 2, SHE Shuai 1, 2, ZHOU Nan3, JIA Pei-xing1,2, ZHANG Jun-guo1, 2* |
1. School of Technology, Beijing Forestry University, Beijing 100083, China
2. Key Lab of State Forestry and Grassland Administration for Forestry Equipment and Automation, Beijing 100083, China
3. State Key Laboratory of Precision Measurement Technology and Instrument, Tianjin University, Tianjin 300072, China |
|
|
Abstract This paper proposes a method to classify and recognize redwood using Terahertz time-domain spectroscopy (THz-TDS). Redwood is expensive and difficult to identify which leads to a shoddy market. The phenomenon disrupts the market order and causes huge economic losses to producers and consumers. The traditional methods of identifying redwood are difficult to give consideration to both accuracy and rapidity, therefore it is necessary to put forward a new method to supplement the traditional classification methods. Compared with the traditional methods, terahertz wave has good penetrability and fingerprint characteristics for redwood, and has great application potential in classification and identification of redwood. In this paper, five kinds of redwood (Dalbergia bariensis, Dalbergia oliveri, Bois de rose, Pterocarpus santalinus, Dalbergia cochinchinensis) are selected as test samples. The THz-TDS system is used to obtain the terahertz time-domain spectrum of wood; the terahertz frequency domain spectrum is obtained by fast Fourier transform of the terahertz time-domain spectrum of five woods, the optical parameters of the terahertz time-domain spectrum are extracted. The results show that different types of wood have time delay line and amplitude difference in time domain spectrum, the attenuation trend and amplitude are different in frequency domain spectrum, the bands of various types of redwood absorption peaks appear differently in the absorption coefficient spectrum, which all can show the differences between various types of wood, indicating that THz-TDS has feasibility for classification of redwood. The successive projections algorithm (SPA) is used to extract the characteristic frequency of the absorption coefficient spectrum and the refractive index spectrum. 28 characteristic frequency points are selected from the 260 frequency points of the absorption coefficient spectrum and the frequency band accounts for 10.77%; 12 characteristic frequency points are selected from 260 frequencies of the refractive index spectrum, and the frequency band accounts for 4.62%. A random forest classification model and a support vector classification model based on the absorption coefficient spectrum and the refractive index spectrum are established and compared. The results show that THz-TDS has great quality to recognize wood. A random forest classification model based on absorption coefficient spectrum and refractive index spectrum shows good classification performance for redwood species and the accuracy rate of classification is 94% and 96% which can show that they can classify and identify redwood species correctly. THz-TDS technique is used to classify and identify mahogany, which provides a new idea and technical scheme for the classification and identification of mahogany therefore it can be used as a supplement to the near-infrared spectrum wood detection method. This method also provides a theoretical basis to apply terahertz technology in the field of wood classification and identification.
|
Received: 2018-09-29
Accepted: 2019-02-06
|
|
Corresponding Authors:
ZHANG Jun-guo
E-mail: zhangjunguo@bjfu.edu.cn
|
|
[1] Wang R, Xie L, Hameed S, et al. Carbon, 2018, 132: 42.
[2] HONG Wei, YU Chao, CHEN Ji-xin, et al(洪 伟,余 超,陈继新,等). Scientia Sinica:Informationis(中国科学: 信息科学), 2016, 46(8): 1086.
[3] Méndez A M, Ali A M, Dennis W, et al. Sensors, 2018, 18(7): 2087.
[4] Zhang H, Li Z, Chen T, et al. J. Appl. Spectrosc., 2018, 85(1): 197.
[5] Tao C, Qin Z, Zhi L, et al. Spectrochim. Acta A, 2018, 205(5): 312.
[6] Bensalem M, Sommier A, Mindeguia J C, et al. J. Infrared Millim. Te., 2018, 39(2): 195.
[7] Zolliker P, Ruggeberg M, Valzania L, et al. IEEE T Thz. Sci. Techn., 2017, 7(6): 722.
[8] ZHANG Wen-tao, WANG Si-yuan, ZHAN Ping-ping, et al(张文涛,王思远,占平平,等). Acta Optica Sinica(光学学报), 2017,(2): 341.
[9] Tang R, Chen X, Li C, et al. Appl. Spectrosc., 2018, 72(3): 3702818755142. |
[1] |
ZHENG Pei-chao, YIN Yi-tong, WANG Jin-mei*, ZHOU Chun-yan, ZHANG Li, ZENG Jin-rui, LÜ Qiang. Study on the Method of Detecting Phosphate Ions in Water Based on
Ultraviolet Absorption Spectrum Combined With SPA-ELM Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 82-87. |
[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] |
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. |
[4] |
CHENG Hui-zhu1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, MA Qian1, 2, ZHAO Yan-chun1, 2. Genetic Algorithm Optimized BP Neural Network for Quantitative
Analysis of Soil Heavy Metals in XRF[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3742-3746. |
[5] |
WAN Mei, ZHANG Jia-le, FANG Ji-yuan, LIU Jian-jun, HONG Zhi, DU Yong*. Terahertz Spectroscopy and DFT Calculations of Isonicotinamide-Glutaric Acid-Pyrazinamide Ternary Cocrystal[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3781-3787. |
[6] |
SHEN Si-cong, ZHANG Jing-xue, CHEN Ming-hui, LI Zhi-wei, SUN Sheng-nan, YAN Xue-bing*. Estimation of Above-Ground Biomass and Chlorophyll Content of
Different Alfalfa Varieties Based on UAV Multi-Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3847-3852. |
[7] |
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. |
[8] |
YAN Xing-guang, LI Jing*, YAN Xiao-xiao, MA Tian-yue, SU Yi-ting, SHAO Jia-hao, ZHANG Rui. A Rapid Method for Stripe Chromatic Aberration Correction in
Landsat Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3483-3491. |
[9] |
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. |
[10] |
DONG Jian-jiang1, TIAN Ye1, ZHANG Jian-xing2, LUAN Zhen-dong2*, DU Zeng-feng2*. Research on the Classification Method of Benthic Fauna Based on
Hyperspectral Data and Random Forest Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3015-3022. |
[11] |
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. |
[12] |
LI Wen-wen1, 2, LONG Chang-jiang1, 2, 4*, LI Shan-jun1, 2, 3, 4, CHEN Hong1, 2, 4. Detection of Mixed Pesticide Residues of Prochloraz and Imazalil in
Citrus Epidermis by Surface Enhanced Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3052-3058. |
[13] |
JIA Zong-chao1, WANG Zi-jian1, LI Xue-ying1, 2*, QIU Hui-min1, HOU Guang-li1, FAN Ping-ping1*. Marine Sediment Particle Size Classification Based on the Fusion of
Principal Component Analysis and Continuous Projection Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3075-3080. |
[14] |
LIU Fei1, TAN Jia-jin1*, XIE Gu-ai2, SU Jun3, YE Jian-ren1. Early Diagnosis of Pine Wilt Disease Based on Hyperspectral Data and Needle Resistivity[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3280-3285. |
[15] |
AN Bai-song1, 2, WANG Xue-mei1, 2*, HUANG Xiao-yu1, 2, KAWUQIATI Bai-shan1, 2. Hyperspectral Estimation of Soil Lead Content Based on Random Frog Band Selection Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3302-3309. |
|
|
|
|