光谱学与光谱分析 |
|
|
|
|
|
Rapid Prediction of Surface Roughness of Natural Polymer Material by Visible/Near Infrared Spectroscopy as a Non-Contact Measurement Method |
YANG Zhong, LIU Ya-na*, Lü Bin, ZHANG Mao-mao |
Research Institute of Wood Industry, Chinese Academy of Forestry, Beijing 100091, China |
|
|
Abstract In order to investigate the feasibility of visible/Near Infrared(Vis-NIR)spectroscopy to predict the surface roughness of natural polymer material(wood) as a non-contact measurement method,the correlations between Vis-NIR spectroscopy and surface roughness measured by contact(stylus) instruments from three different sections of wood samples were analyzed. The results showed that the surface roughness parameters, arithmetical mean deviation of profile (Ra), ten-point height of irregularities (Rz) and the maximum height of profile (Ry), of wood samples were successfully predicted by using Vis-NIR (400~2 500 nm) models from the three sections of the samples. The correlations between values measured by the stylus instruments and the values predicted by the models were good. The correlation coefficients of Rz reached up to 0.92. Compared to the models based on the Vis-NIR from the radial section and tangential section of the samples, the predictive effect of the model based on cross section was the best. The correlation coefficients between the values measured by the stylus instruments and the values predicted by the models based on different spectrum wavelength range, 400~780, 780~1 100, 1 100~2 500, 780~2 500 and 400~2 500 nm, were generally above 0.80. The prediction results of the model based on spectrum wavelength range 400~2 500 nm was better than the models based on the other spectrum wavelength ranges. The results showed that the predictive effect was not improved by pretreatment of the spectrum. It is proposed to use the original spectrum to predict the surface roughness of natural polymer material.
|
Received: 2012-08-08
Accepted: 2012-11-20
|
|
Corresponding Authors:
LIU Ya-na
E-mail: liuyana77@gmail.com
|
|
[1] LIU Bin, FENG Qi-bo, KUANG Cui-fang(刘 斌, 冯其波, 匡萃方). Optical Instruments(光学仪器), 2004, 26(5): 55. [2] Cahill B, Baradie M A El. Journal of Materials Processing Technology, 2001, 119(1-3): 299. [3] Lu R S, Tian G Y. Measurement Science and Technology, 2006, 17(6): 1496. [4] Mitri F G, Kinnick R R, Greenleaf J F, et al. Ultrasonics, 2009, 49(1): 10. [5] Lee K C, Ho S J, Ho S Y. Precision Engineering, 2005, 29(1): 95. [6] Chang S I, Ravathur J S. Quality Engineering, 2005, 17(3): 435. [7] Francesca A, Federico P, Graziella P, et al. Food and Bioprocess Technology, 2011, 4(5): 809. [8] Wu Y W, Sun S Q, Zhou Q, et al. Journal of Pharmaceutical and Biomedical Analysis, 2008, 46(3): 498. [9] Schimleck L R, Payne P, Wearne R H. Wood and Fiber Science, 2005, 37(3): 462. [10] Hein P R G, Campos A C M, Mendes R F, et al. European Journal of Wood and Wood Products, 2011, 69(3): 431. [11] Taylor A M, Labbé N, Noehmer A. Holzforschung, 2011, 65(2): 85. [12] Yang Z, Lü B, Fu Y J. Advanced Materials Research, 2012, (479-481): 1772. |
[1] |
MA Fang1, HUANG An-min2, ZHANG Qiu-hui1*. Discrimination of Four Black Heartwoods Using FTIR Spectroscopy and
Clustering Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1915-1921. |
[2] |
CHEN Jin-hao, JIANG Da-peng, ZHANG Yi-zhuo*, WANG Ke-qi*. Research on Data Migration Modeling Method for Bending Strength of
Solid Wood Based on SWCSS-GFK-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1471-1477. |
[3] |
HAN Liu-yang1, 2, 3, HAN Xiang-na1, TIAN Xing-ling4, ZHOU Hai-bin2, 5, YIN Ya-fang2, 3, GUO Juan2, 3*. Effects of Three Kinds of Consolidants on the Micromechanical Properties of Archaeological Wood From “Xiaobaijiao Ⅰ” Shipwreck by Infrared Spectroscopy and Thermogravimetric Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1529-1534. |
[4] |
FU Yan-hua1, LIU Jing2*, MAO Ya-chun2, CAO Wang2, HUANG Jia-qi2, ZHAO Zhan-guo3. Experimental Study on Quantitative Inversion Model of Heavy Metals in Soda Saline-Alkali Soil Based on RBF Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1595-1600. |
[5] |
MAO Ya-chun1, WEN Jian1*, FU Yan-hua2, CAO Wang1, ZHAO Zhan-guo3, DING Rui-bo1. Quantitative Inversion Model Based on the Visible and Near-Infrared Spectrum for Skarn-Type Iron Ore[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 68-73. |
[6] |
HUANG Ke-jia1, DU Jing2, ZHU Jian3*, LI Nai-sheng2, CHEN Yue2, WU Yuan-yuan4. Mapping Analysis by μ-X-Ray Fluorescence for Waterlogged Archaeological Wood From “Nanhai No.1” Shipwreck[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(09): 2930-2933. |
[7] |
WANG Cheng-kun1, ZHAO Peng1,2*. Study on Simultaneous Classification of Hardwood and Softwood Species Based on Spectral and Image Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(06): 1713-1721. |
[8] |
ZHAO Peng1,2*, HAN Jin-cheng1, WANG Cheng-kun1. Wood Species Classification With Microscopic Hyper-Spectral Imaging Based on I-BGLAM Texture and Spectral Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(02): 599-605. |
[9] |
WANG Dong, LIU Shan-jun*, MAO Ya-chun, LI Heng-yu, QI Yu-xin. Effect of Grade on Reflectance Spectra of Anshan Iron Mine[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(10): 3193-3198. |
[10] |
YU Lei, CHEN Jin-hao, LI Long-fei, LI Chao*, ZHANG Yi-zhuo*. Prediction Model of Wood Absolute Dry Density by Near-Infrared Spectroscopy Based on IPSO-BP[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(09): 2937-2942. |
[11] |
YUAN Cheng, ZHAI Sheng-cheng*, ZHANG Yi-meng, ZHANG Yao-li. Simple Evaluation of the Degradation State of Archaeological Wood Based on the Infrared Spectroscopy Combined With Thermogravimetry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(09): 2943-2950. |
[12] |
MAO Ya-chun1,2, DING Rui-bo1*, LIU Shan-jun1,2, BAO Ni-sha1,2. Research on Inversion Model of Low-Grade Porphyry Copper Deposit Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(08): 2474-2478. |
[13] |
ZHAO Peng*, TANG Yan-hui, LI Zhen-yu. Wood Species Recognition with Microscopic Hyper-Spectral Imaging and Composite Kernel SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(12): 3776-3782. |
[14] |
ZHAO Peng, LI Yue. Simultaneous Prediction of Wood Density and Wood Species Based on Visible/Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(11): 3525-3532. |
[15] |
YU Mo-li, LIU Shan-jun*, SONG Liang, HUANG Jian-wei, LI Tian-zi, WANG Dong. Spectral Characteristics and Remote Sensing Model of Tailings with Different Water Contents[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(10): 3096-3101. |
|
|
|
|