Advanced Search+
Chengxu LU (吕程序), Bo WANG (王博), Xunpeng JIANG (姜训鹏), Junning ZHANG (张俊宁), Kang NIU (牛康), Yanwei YUAN (苑严伟). Detection of K in soil using time-resolved laser-induced breakdown spectroscopy based on convolutional neural networks[J]. Plasma Science and Technology, 2019, 21(3): 34014-034014. DOI: 10.1088/2058-6272/aaef6e
Citation: Chengxu LU (吕程序), Bo WANG (王博), Xunpeng JIANG (姜训鹏), Junning ZHANG (张俊宁), Kang NIU (牛康), Yanwei YUAN (苑严伟). Detection of K in soil using time-resolved laser-induced breakdown spectroscopy based on convolutional neural networks[J]. Plasma Science and Technology, 2019, 21(3): 34014-034014. DOI: 10.1088/2058-6272/aaef6e

Detection of K in soil using time-resolved laser-induced breakdown spectroscopy based on convolutional neural networks

Funds: This work is supported by National Natural Science Foundation of China (Grant No. 61505253) and National Key Research and Development Plan of China (Project No. 2016YFD0200601).
More Information
  • Received Date: July 30, 2018
  • One of the technical bottlenecks of traditional laser-induced breakdown spectroscopy (LIBS) is the difficulty in quantitative detection caused by the matrix effect. To troubleshoot this problem, this paper investigated a combination of time-resolved LIBS and convolutional neural networks (CNNs) to improve K determination in soil. The time-resolved LIBS contained the information of both wavelength and time dimension. The spectra of wavelength dimension showed the characteristic emission lines of elements, and those of time dimension presented the plasma decay trend. The one-dimensional data of LIBS intensity from the emission line at 766.49 nm were extracted and correlated with the K concentration, showing a poor correlation of R2c=0.0967, which is caused by the matrix effect of heterogeneous soil. For the wavelength dimension, the two-dimensional data of traditional integrated LIBS were extracted and analyzed by an artificial neural network (ANN), showing R2v=0.6318 and the root mean square error of validation (RMSEV)=0.6234. For the time dimension, the two-dimensional data of time-decay LIBS were extracted and analyzed by ANN, showing R2v=0.7366 and RMSEV=0.7855. These higher determination coefficients reveal that both the non-K emission lines of wavelength dimension and the spectral decay of time dimension could assist in quantitative detection of K. However, due to limited calibration samples, the two-dimensional models presented over-fitting. The three-dimensional data of time-resolved LIBS were analyzed by CNNs, which extracted and integrated the information of both the wavelength and time dimension, showing the R2v=0.9968 and RMSEV=0.0785. CNN analysis of time-resolved LIBS is capable of improving the determination of K in soil.
  • [1]
    Chérel I et al 2014 J. Exp. Bot. 65 833
    [2]
    ISO 2009 Soil quality—Determination of trace elements in extracts of soil by ICP –AES: ISO 22036 2008 https://iso. org/standard/40653.html
    [3]
    ISO 2013 Soil quality—Determination of trace elements using ICP-MS: ISO/TS 16965 2013 https://iso.org/standard/ 58056.html
    [4]
    Hussain T et al 2007 Environ. Monit. Assess. 124 131
    [5]
    Pareja J et al 2013 Appl. Opt. 52 2470
    [6]
    Meng D S et al 2014 Chin. J. Lasers 41 0515003 (in Chinese)
    [7]
    Dong D M et al 2013 Spectrosc. Spectr. Anal. 33 785 (in Chinese)
    [8]
    Zhang J N et al 2014 Trans. Chin. Soc. Agric. Mach. 45 294 (in Chinese)
    [9]
    Yu K et al 2017 Spectrosc. Spectr. Anal. 37 2879 (in Chinese)
    [10]
    Guezenoc J et al 2017 Spectrochim. Acta Part B 134 6
    [11]
    El Rakwe M et al 2017 J. Chemom. 31 e2869
    [12]
    Noll R 2012 Laser-Induced Breakdown Spectroscopy (Berlin, Heidelberg: Springer)
    [13]
    Bredice F et al 2017 Spectrochim. Acta Part B 135 48
    [14]
    Rawat W and Wang Z H 2017 Neur. Comput. 29 2352
    [15]
    Yao G L, Lei T and Zhong J D 2018 Patt. Recognit. Lett. (https://doi.org/10.1016/j.patrec.2018.05.018)
    [16]
    Liu J C et al 2017 Analyst 142 4067
    [17]
    Hiwa S et al 2016 Comput. Intell. Neurosci. 2016 1841945
    [18]
    Windrim L et al 2017 IEEE Trans. Geosci. Remote Sens. 56 2798
    [19]
    Snee R D 1977 Technometrics 19 415
    [20]
    Wang C et al 2018 Spectrosc. Spectr. Anal. 38 36 (in Chinese)
    [21]
    Kim P 2017 MATLAB Deep Learning: with Machine Learning, Neural Networks and Artificial Intelligence (Berkeley, CA: Springer)
    [22]
    Glotot X and Bengio Y 2010 Understanding the difficulty of training deep feedforward neural networks Proc. of the 13th Int. Conf. on Artificial Intelligence and Statistics (Chia Laguna Resort, Sardinia, Italy: AISTATS)
    [23]
    Maas A L, Hannun A Y and Ng A Y 2013 Rectifier nonlinearities improve neural network acoustic models Proc. of the 30th Int. Conf. on Machine Learning Workshop on Deep Learning (Atlanta, GA, USA: ICML)
  • Related Articles

    [1]Huihui WANG (王慧慧), Zun ZHANG (张尊), Kaiyi YANG (杨凯翼), Chang TAN (谭畅), Ruilin CUI (崔瑞林), Jiting OUYANG (欧阳吉庭). Axial profiles of argon helicon plasma by optical emission spectroscope and Langmuir probe[J]. Plasma Science and Technology, 2019, 21(7): 74009-074009. DOI: 10.1088/2058-6272/ab175b
    [2]Zilu ZHAO (赵紫璐), Dezheng YANG (杨德正), Wenchun WANG (王文春), Hao YUAN (袁皓), Li ZHANG (张丽), Sen WANG (王森). Volume added surface barrier discharge plasma excited by bipolar nanosecond pulse power in atmospheric air: optical emission spectra influenced by gap distance[J]. Plasma Science and Technology, 2018, 20(11): 115403. DOI: 10.1088/2058-6272/aac881
    [3]Gao ZHAO (赵高), Wanying ZHU (朱婉莹), Huihui WANG (王慧慧), Qiang CHEN (陈强), Chang TAN (谭畅), Jiting OUYANG (欧阳吉庭). Study of axial double layer in helicon plasma by optical emission spectroscopy and simple probe[J]. Plasma Science and Technology, 2018, 20(7): 75402-075402. DOI: 10.1088/2058-6272/aab4f1
    [4]WU Zhonghang (吴忠航), LIANG Rongqing (梁荣庆), Masaaki NAGATSU (永津雅章), CHANG Xijiang (昌锡江). The Characteristics of Columniform Surface Wave Plasma Excited Around a Quartz Rod by 2.45 GHz Microwaves[J]. Plasma Science and Technology, 2016, 18(10): 987-991. DOI: 10.1088/1009-0630/18/10/04
    [5]LAN Hui (兰慧), WANG Xinbing (王新兵), ZUO Duluo (左都罗). Time-Resolved Optical Emission Spectroscopy Diagnosis of CO2 Laser-Produced SnO2 Plasma[J]. Plasma Science and Technology, 2016, 18(9): 902-906. DOI: 10.1088/1009-0630/18/9/05
    [6]CHEN Dan (陈聃), ZENG Xinwu (曾新吾), WANG Yibo (王一博). The Optical Diagnosis of Underwater Positive Sparks and Corona Discharges[J]. Plasma Science and Technology, 2014, 16(12): 1100-1105. DOI: 10.1088/1009-0630/16/12/04
    [7]A. SAEED, A. W. KHAN, M. SHAFIQ, F. JAN, M. ABRAR, M. ZAKA-UL-ISLAM, M. ZAKAULLAH. Investigation of 50 Hz Pulsed DC Nitrogen Plasma with Active Screen Cage by Trace Rare Gas Optical Emission Spectroscopy[J]. Plasma Science and Technology, 2014, 16(4): 324-328. DOI: 10.1088/1009-0630/16/4/05
    [8]WU Zhonghang(吴忠航), LI Zebin(李泽斌), JU Jiaqi(居家奇), HE Kongduo(何孔多), YANG Xilu(杨曦露), YAN Hang(颜航), CHEN Zhenliu(陈枕流), OU Qiongrong(区琼荣), LIANG Rongqing(梁荣庆). Experimental Investigation of Surface Wave Plasma Excited by a Cylindrical Dielectric Rod[J]. Plasma Science and Technology, 2014, 16(2): 118-122. DOI: 10.1088/1009-0630/16/2/06
    [9]LIU Wenyao (刘文耀), ZHU Aimin (朱爱民), Li Xiaosong (李小松), ZHAO Guoli (赵国利), et al.. Determination of Plasma Parameters in a Dual-Frequency Capacitively Coupled CF 4 Plasma Using Optical Emission Spectroscopy[J]. Plasma Science and Technology, 2013, 15(9): 885-890. DOI: 10.1088/1009-0630/15/9/10
    [10]CHEN Zhaoquan (陈兆权), LIU Minghai (刘明海), HU Yelin (胡业林), ZHENG Xiaoliang (郑晓亮), LI Ping (李平), XIA Guangqing (夏广庆). Character Diagnosis for Surface-Wave Plasmas Excited by Surface Plasmon Polaritons[J]. Plasma Science and Technology, 2012, 14(8): 754-758. DOI: 10.1088/1009-0630/14/8/13

Catalog

    Article views (163) PDF downloads (216) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return