激光诱导击穿光谱的自组织特征映射结合相关判别对天然地质样品分类方法研究

doi:10.3964/j.issn.1000-0593(2018)06-1874-06

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Abstract Laser-induced breakdown spectroscopy has the characteristics of small-invasive, in situ and rapid analysis. It has wide application prospects in the field of sample identification and component analysis. In order to explore the feasibility of the technology in the automatic identification of natural geological samples, a method of identifying and sorting LIBS spectral of natural geological samples by self-organizing feature map neural network combined with correlation is proposed in this paper. In order to reduce the interference of unrelated data such as background noise in the whole spectrum and the computational complexity, the feature spectral line is extracted on the basis of elemental to achieve the dimensionality reduction of high dimensional spectral data. The network training model is established by using the feature spectrum data as input, then the weight vectors which have the feature of input samples are obtained. Finally the geological sample classification is achieved by the correlation analysis between the weight vectors and the samples to be tested. The classification results of the 16 kinds of natural geological samples prove that the feature spectrum is superior to full spectrum and PCA dimension reduction, especially in the aspects of descending dimension and extracting the main features of data. The algorithm proposed in this paper has a better classification effect on the feature spectrum data of 16 samples than SVM and SOM neural network algorithm. Moreover, the validity of the proposed method is initially verified in this paper.

Key words：LIBS；Feature spectral line；Self-organizing feature map；Correlation analysis；Classification and recognition

Received: 2017-11-06 Accepted: 2018-03-09

ZTFLH:

TP391

Corresponding Authors: BI Yun-feng E-mail: byf@sdu.edu.cn

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2018)06-1874-06 OR https://www.gpxygpfx.com/EN/Y2018/V38/I06/1874

[1] GAO Jie，SHENG Cheng，ZHUO Shang-jun(高捷，盛成，卓尚军). Metallurgical Analysis(冶金分析)，2015，35(2)：74.
[2] GONG Rui-kun，WANG Xiao-lei，ZHANG Li-wei(龚瑞昆，王晓磊，张励维). Journal of North China University of Science and Techology(华北理工大学学报)，2016，38(4)：91.
[3] ZHU Yi-ning，LI Jia-ming，GUO Lian-bo，et al(朱毅宁，李嘉铭，郭连波，等). Chinese Journal of Analytical Chemistry(分析化学研究报告)，2017，45(3)：336.
[4] YU Yang，HAO Zhong-qi，ZENG Qing-dong，et al(于洋，郝中骐，曾庆栋，等). Acta Physica Sinica(物理学报)，2013，62(21)：215201-3.
[5] CHEN Xing-long，DONG Feng-zhong，TAO Guo-qiang，et al.(陈兴龙，董凤忠，陶国强，等). Chinese Journal of Lasers(中国激光)，2013，40(12)：1215001-3.
[6] PENG Guang-min，CHEN Ting(彭广民，陈婷). Geomatics and Spatial Information Technology(测绘与空间地理信息)，2016，39(7)：25.
[7] YANG Chong-rui，WANG Jia-sheng，SHENG Xin-zhi，et al(杨崇瑞，汪家升，盛新志，等). Infrared and Laser Engineering(红外与激光工程)，2014，43(11)：3809.
[8] ZHU Yuan-shuo，LI Ying，LU Yuan，et al(朱元硕，李颖，卢渊，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2017，37(9)：2892.
[9] DONG Chen-zhong，YANG Feng，SU Mao-yin(董晨钟，杨峰，苏茂银). Journal of Northwest Normal University·Natural Science(西北师范大学学报·自然科学版)，2015，51(1)：45.
[10] CHEN Tie-ming，MA Ji-xia，CAI Jia-mei，et al(陈铁明，马继霞，蔡家楣，等). Journal of Computer Research and Development(计算机研究与发展)，2012，49(4)：735.
[11] LIU Xiao-na，ZHANG Qiao，SHI Xin-yuan，et al(刘晓娜，张乔，史新元，等). Chinese Journal of Traditional Chinese Medicine(中华中医药杂志)，2015，30(5)：1612.
[12] KE Zhi-quan，WANG Yang-en，WANG Shao-long，et al(柯梽全，王阳恩，王绍龙，等). SCIENTIA SINICA Physica, Mechanica & Astronomica(中国科学：物理力学天文学)，2015，45(8)：084204.