光谱学与光谱分析 |
|
|
|
|
|
Influence of Different Coal Particle Sizes on Near-Infrared Spectral Quantitative Analytical Models |
LEI Meng1, LI Ming1*, WU Nan2, LI Ying-na3, CHENG Yu-hu1 |
1. School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221008, China 2. Caofeidian Port Office of Hebei Inspection and Quarantine Bureau, Tangshan 063611, China 3. Environmental and Chemical Engineering Department, Tangshan College, Tangshan 063000, China |
|
|
Abstract In order to reduce the errors of near-infrared spectral acquisition, analytical models of coal spectra with different particle sizes, 0.2, 1, 3 and 13 mm, were studied in this paper. The feature information of spectra was extracted by PCA method, then two quantitative analytical models were established based on GA-BP and GA-Elman neural network algorithms. Through spectral preprocessing with data normalization and multiplicative scatter correction methods, the results showed that with the 0.2 mm size, the correlations between spectra and the standard value were the strongest, and the analytical precision of models were the best. But for smoothed spectra, the models, under 1 mm size, were better than others. Smoothing method was not suitable for the spectra with less obvious wave crest characteristics, while multiplicative scatter correction method was better. According to original spectra, particle size of 0.2 mm had the highest accuracy, followed by 1 and 3 mm and the worst was under 13 mm. Overall, the larger the size for coal particle, the more the unstable factors for spectra, increasing negative influences on analytical models.
|
Received: 2012-05-23
Accepted: 2012-09-15
|
|
Corresponding Authors:
LI Ming
E-mail: liming@cumt.edu.cn
|
|
[1] Kim Dongwon, Lee Jongmin, Kim Jaesung. J. Chem. Eng., 2009, 26(2): 489. [2] Li Ming, Xu Zhibin, Yu Lei, et al. Proceedings of 2009 Chinese Control and Decision Conference, 2009: 194. [3] Vohland Michael, Michel Kerstin, Ludwig Bernard. Journal of Plant of Plant Nutrition and Soil Science, 2011, 174(5): 695. [4] CHENG Fang, FAN Yu-xia, LIAO Yi-tao(成 芳, 樊玉霞, 廖宜涛). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2012, 32(2): 354. [5] YANG Hui-hua, GUO Tuo, MA Jin-fang, et al(杨辉华 ,郭 拓, 马晋芳,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2012, 32(5): 1247. [6] Andres J M, Bona M T. Analytica Chimica Acta, 2005, 535: 123. [7] Fan Wei, Li Hongdong, Shan Yang, et al. Analytical Methods, 2011, 3: 1872. [8] Kodzue Kinoshita, Hiroyuki Morita, Mari Miyazaki, et al. Analytical Methods, 2010, 2: 1671. [9] HE Xuan-ming(何选明). Coal Chemistry(Second Edition)(煤化学, 第2版). Beijing: Metallurgical Industry Press(北京: 冶金工业出版社), 2010, 5. [10] National Standard of the People’s Republic of China(中华人民共和国国家标准). GB 474—2008, Method for Preparation of Coal Sample(煤样的制备方法),2008. [11] National Standard of the People’s Republic of China(中华人民共和国国家标准). GB/T 212—2008, Proximate Analysis of Coal(煤的工业分析方法),2008. [12] Mattle Christian, Heigl Nico, Abel Gudrun, et al. JPC-Journal of Planar Chromatography-Modern TLC, 2010, 23(5): 348. [13] XIE Jun, PAN Tao, CHEN Jie-mei(谢 军, 潘 涛, 陈洁梅). Chinese Journal of Analytical Chemistry(分析化学), 2010, 38(3): 342. [14] Niu Xiaoying, Zhao Zhilei, Jia Kejun. Food Chemistry, 2012, 133(2): 592. [15] Cui Anqi, Xu Hua, Jia Peifa. Expert Systems with Applications, 2011, 38(7): 8186. [16] Ding Shifei, Su Chunyang, Yu Junzhao. Artificial Intelligence Review, 2011, 36(2): 153. |
[1] |
LI Xin-ting, ZHANG Feng, FENG Jie*. Convolutional Neural Network Combined With Improved Spectral
Processing Method for Potato Disease Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 215-224. |
[2] |
WANG Yu-qi, LI Bin, ZHU Ming-wang, LIU Yan-de*. Optimizations of Sample and Wavelength for Apple Brix Prediction Model Based on LASSOLars Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1419-1425. |
[3] |
LIU Yu-juan1, 2, 3 , LIU Yan-da1, 2, 3, SONG Ying1, 2, 3*, ZHU Yang1, 2, 3, MENG Zhao-ling1, 2, 3. Near Infrared Spectroscopic Quantitative Detection and Analysis Method of Methanol Gasoline[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1489-1494. |
[4] |
JIANG Xiao-gang1, ZHU Ming-wang1, YAO Jin-liang1, LI Bin1, LIAO Jun1, LIU Yan-de1*, ZHANG Jian-yi2, JING Han-song2. Research on Parameter Optimization of Apple Sugar Model Based on Near-Infrared On-Line Device[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 116-121. |
[5] |
CAI Yu1, 2, ZHAO Zhi-fang3, GUO Lian-bo4, CHEN Yun-zhong1, 2*, JIANG Qiong4, LIU Si-min1, 2, ZHANG Cong-zi4, KOU Wei-ping5, HU Xiu-juan5, DENG Fan6, HUANG Wei-hua7. Research on Origin Traceability of Rhizoma Dioscoreae Based on LIBS[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 138-144. |
[6] |
XU Lu1, CHEN Yi-yun1, 2, 3*, HONG Yong-sheng1, WEI Yu1, GUO Long4, Marc Linderman5. Estimation of Soil Organic Carbon Content by Imaging Spectroscopy With Soil Roughness[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(09): 2788-2794. |
[7] |
YANG Jie-kai1, GUO Zhi-qiang1, HUANG Yuan2, 3*, GAO Hong-sheng1, JIN Ke1, WU Xiang-shuai2, YANG Jie1. Early Classification and Detection of Melon Graft Healing State Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(07): 2218-2224. |
[8] |
HE Nian, SHAN Peng*, HE Zhong-hai, WANG Qiao-yun, LI Zhi-gang, WU Zhui. Study on the Fractional Baseline Correction Method of ATR-FTIR
Spectral Signal in the Fermentation Process of Sodium Glutamate[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1848-1854. |
[9] |
XU Bo1, XU Tong-yu1, 2*, YU Feng-hua1, 2, ZHANG Guo-sheng1, FENG Shuai1, GUO Zhong-hui1, ZHOU Chang-xian1. Inversion Method for Cellulose Content of Rice Stem in Northeast Cold Region Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(06): 1775-1781. |
[10] |
LI Xue-ying1, 2, 3, 4, FAN Ping-ping1, 3, 4*, HOU Guang-li1, 3, 4, QIU Hui-min1, 3, 4, LÜ Hong-min1, 3, 4. A Review of Calibration Transfer Based on Spectral Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(04): 1114-1118. |
[11] |
LI Qing-bo1, BI Zhi-qi1, SHI Dong-dong2. The Method of Fishmeal Origin Tracing Based on EDXRF Spectrometry Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(03): 745-749. |
[12] |
LI Xin-xing1, LIANG Bu-wen1, BAI Xue-bing1, LI Na2*. Research Progress of Spectroscopy in the Detection of Soil Moisture Content[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(12): 3705-3710. |
[13] |
LÜ Mei-rong1, REN Guo-xing1, 2, LI Xue-ying1, FAN Ping-ping1, LIU Jie1, SUN Zhong-liang1, HOU Guang-li1, LIU Yan1*. The Effect of Spectral Pretreatment on the LSSVM Model of Nitrogen in Intertidal Sediments[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(08): 2409-2414. |
[14] |
MA Ben-xue1,2*, YU Guo-wei1,2, WANG Wen-xia1,2, LUO Xiu-zhi1,2, LI Yu-jie1,2, LI Xiao-zhan1,2, LEI Sheng-yuan1,2. Recent Advances in Spectral Analysis Techniques for Non-Destructive Detection of Internal Quality in Watermelon and Muskmelon: A Review[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(07): 2035-2041. |
[15] |
LUO Long-qiang1, YAO Xin-li1, HE Sai-ling1, 2*. Study on the Method of Determining the Survival Rate of Rice Seeds Based on Visible-Near Infrared Multispectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(01): 221-226. |
|
|
|
|