光谱学与光谱分析 |
|
|
|
|
|
Rapid Soil Classification with Laser Induced Breakdown Spectroscopy |
MENG De-shuo, ZHAO Nan-jing*, MA Ming-jun, GU Yan-hong, YU Yang, FANG Li, WANG Yuan-yuan, JIA Yao, LIU Wen-qing, LIU Jian-guo |
Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031,China |
|
|
Abstract Soil classification is an important research content in soil science field. It is the basis of soil survey and resource evaluation which is important to agricultural production. There are many soil classification standards all over the world. China has two kinds of classifications including soil genetic classification and soil system classification. There are great differences between different types of soil elements, so it is feasible for soil classification to use laser induced breakdown spectroscopy. Laser induced breakdown spectroscopy (LIBS) is a new element analysis technology which uses a laser pulse with high energy density to ablate samples. LIBS has been used in many fields including environmental protection and industrial production control. It can directly reflect the difference of element content in different soils. The experimental setup including an Nd: YAG laser, a spectrometer, a computer and a rotating platform. In the experiment 7 kinds of soil (red soil, brick red soil, lateritic red soil, paddy soil, cinnamon, alluvial soil and alpine meadow soil) including 25 samples were used. All soil samples were grinded and sieved before the experiment.Under the same experimental condition, the temperatures of the plasma created by the laser pulses on the surface of the different soil samples have great differences. The lateritic red soil had the highest temperature, and the alpine meadow soil had the lowest. But it was not enough to form the basis for classification. Therefore six constant elements including Si, Fe, Al, Mg, Ca and Ti were selected and their spectral line intensity were treated as classification index. Principal component analysis (PCA) was used to simplify the classification process. The PCA method could simplify the 6 indexes to few independent indexes which could also reflect the spectral information of the 6 elements. The original spectral data was processed by Matlab. The process consisted of spectral background removal, characteristic spectrum identify and extraction. The classification results showed a three--dimensional figure. Except alpine meadow soil which varied in element concentrations 6 kinds of soils achieved good classification. The brick red soil and lateritic red soil varied in PC1, but their PC2s and PC3s were the same. The two kinds of soil overlapped with each other and they couldn’t be separated. Back-propagation artificial network was also used to achieve soil classification. The classification results were the same with the PCA. Some brick red soil and lateritic red soil samples were identified inaccurately. When the PC1, 2, 3 were used as the input of the BP-neural network, the classification had much better accuracy because less input improved the performance of the BP-neural network. Only one alpine meadow soil sample was identified to cinnamon soil. When the plasma temperature was also taken into account, all the soil samples could be distinguished. The results showed that LIBS could be used to classify soils based on their element content differences. The PCA, soil plasma temperature and BP-neural network were useful tools to achieve soil classification. The LIBS provides a useful tool for general detailed soil survey and rational utilization of soil.
|
Received: 2015-10-13
Accepted: 2016-03-06
|
|
Corresponding Authors:
ZHAO Nan-jing
E-mail: njzhao@aiofm.ac.cn
|
|
[1] GONG Zi-tong, ZHANG Gan-lin, CHEN Zhi-cheng,et al(龚子同,张甘霖,陈志诚,等). Chinese Journal of Soil Science(土壤通报),2002, 33(1): 1. [2] CHEN Huai-man(陈怀满). Environmental Soil Science(环境土壤学). Beijing: Science Press(北京:科学出版社), 2010. 90. [3] SHI Zhou, WANG Qian-long, PENG Jie, et al(史 舟,王乾龙,彭 杰,等). Science China: Earth Science(中国科学:地球科学),2014, 44(5):978. [4] Vasques G M, Dematte J A, Viscarra Rossel, et al. Geoderma,2014, 223(7): 73. [5] Dematte Jose A M, Bellinaso H, Romero D J, et al. Science Agricola,2014, 71(6): 509. [6] ZHANG Si-chong, ZHOU Xiao-cong, YE Hua-xiang, et al(张思冲,周晓聪,叶华香,等). Chinese Agricultural Science Bulletin(中国农学通报),2009, 25(13):230. [7] Michela Corsi, Gabriele Cristoforetti, Montserrat Hidalgo, et al. Applied Geochemistry,2006,21: 748. [8] Gaft Michael,Nagli, et al. Applied Spectroscopy, 2014, 68(9): 1004. [9] Dutouquet C, Gallou G, Le Bihan O, et al. Talanta, 2014, 127(1): 75. [10] El Haddad J, Bruyere D, Ismael A. Spectrochimica Acta Part B: Atomic Spectroscopy, 2014, 97(1): 57. [11] ZHANG Jun-ning, FANG Xian-fa, ZHANG Xiao-chao, et al(张俊宁,方宪法,张小超,等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报),2014, 45(10):294. [12] HUANG Ying-feng, LIU Teng-hui(黄应丰,刘腾辉). Acta Pedologica Sinica(土壤学报),1995, 32(1): 58. [13] HU Zhen-hua, ZHANG Qiao, DING Lei, et al(胡振华,张 巧,丁 蕾,等). Acta Optica Sinica(光学学报),2013, 33(4): 295. [14] YU Qi, MA Xiao-hong, WANG Rui, et al(余 琦,马晓红,王 锐,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2014,34(11): 3095. [15] SUN Lan-xiang, YU Hai-bin, CONG Zhi-bo, et al(孙兰香,于海滨,从智博,等). Acta Optica Sinica(光学学报), 2010, 30(9): 2757.
|
[1] |
LIU Jia1, 2, GUO Fei-fei2, YU Lei2, CUI Fei-peng2, ZHAO Ying2, HAN Bing2, SHEN Xue-jing1, 2, WANG Hai-zhou1, 2*. Quantitative Characterization of Components in Neodymium Iron Boron Permanent Magnets by Laser Induced Breakdown Spectroscopy (LIBS)[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 141-147. |
[2] |
GUO Ya-fei1, CAO Qiang1, YE Lei-lei1, ZHANG Cheng-yuan1, KOU Ren-bo1, WANG Jun-mei1, GUO Mei1, 2*. Double Index Sequence Analysis of FTIR and Anti-Inflammatory Spectrum Effect Relationship of Rheum Tanguticum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 188-196. |
[3] |
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. |
[4] |
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. |
[5] |
YANG Wen-feng1, LIN De-hui1, CAO Yu2, QIAN Zi-ran1, LI Shao-long1, ZHU De-hua2, LI Guo1, ZHANG Sai1. Study on LIBS Online Monitoring of Aircraft Skin Laser Layered Paint Removal Based on PCA-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3891-3898. |
[6] |
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. |
[7] |
FANG Zheng, WANG Han-bo. Measurement of Plastic Film Thickness Based on X-Ray Absorption
Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3461-3468. |
[8] |
XU Rong1, AO Dong-mei2*, LI Man-tian1, 2, LIU Sai1, GUO Kun1, HU Ying2, YANG Chun-mei2, XU Chang-qing1. Study on Traditional Chinese Medicine of Lonicera L. Based on Infrared Spectroscopy and Cluster Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3518-3523. |
[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] |
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. |
[11] |
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. |
[12] |
XUE Fang-jia, YU Jie*, YIN Hang, XIA Qi-yu, SHI Jie-gen, HOU Di-bo, HUANG Ping-jie, ZHANG Guang-xin. A Time Series Double Threshold Method for Pollution Events Detection in Drinking Water Using Three-Dimensional Fluorescence Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3081-3088. |
[13] |
SUN Cheng-yu1, JIAO Long1*, YAN Na-ying1, YAN Chun-hua1, QU Le2, ZHANG Sheng-rui3, MA Ling1. Identification of Salvia Miltiorrhiza From Different Origins by Laser
Induced Breakdown Spectroscopy Combined with Artificial Neural
Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3098-3104. |
[14] |
LIU Shu1, JIN Yue1, 2, SU Piao1, 2, MIN Hong1, AN Ya-rui2, WU Xiao-hong1*. Determination of Calcium, Magnesium, Aluminium and Silicon Content in Iron Ore Using Laser-Induced Breakdown Spectroscopy Assisted by Variable Importance-Back Propagation Artificial Neural Networks[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3132-3142. |
[15] |
JIA Hao1, 3, 4, ZHANG Wei-fang1, 3, LEI Jing-wei1, 3*, LI Ying-ying1, 3, YANG Chun-jing2, 3*, XIE Cai-xia1, 3, GONG Hai-yan1, 3, DING Xin-yu1, YAO Tian-yi1. Study on Infrared Fingerprint of the Classical Famous
Prescription Yiguanjian[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3202-3210. |
|
|
|
|