[1]孙淼,Guindo Mahamed Lamine,庄振华,等. 基于LIBS技术和卷积神经网络的土壤铅含量等级快速分类[J].浙江科技学院学报,2019,(05):373-380.
　SUN Miao,Guindo Mahamed Lamine,ZHUANG Zhenhua,et al. On fast classification of lead(Pb) levels in soil based on LIBS technology and convolutional neural network[J].,2019,(05):373-380.
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《浙江科技学院学报》[ISSN:1001-3733/CN:61-1062/R]

卷:
期数:
2019年05期
页码:
373-380
栏目:
出版日期:
2019-10-31

文章信息/Info

Title:
 On fast classification of lead(Pb) levels in soil based on LIBS technology and convolutional neural network
文章编号:
1671-8798(2019)05-0373-08
作者:
 孙淼; Guindo Mahamed Lamine; 庄振华; 仲海鹏; 赵芸
 浙江科技学院 信息与电子工程学院，杭州 310023
Author(s):
 SUN Miao;  Guindo Mahamed Lamine;  ZHUANG Zhenhua;  ZHONG Haipeng;  ZHAO Yun
 School of information and electronic engineering, Zhejiang University of Science and Technology, Hangzhou 310023, Zhejiang, China
关键词:
 ; 激光诱导光谱技术; 卷积神经网络; 快速分类; 土壤修复
分类号:
TP183；S153.6
文献标志码:
A
摘要:
 将激光诱导击穿光谱（LIBS）技术与卷积神经网络（CNN）相结合用于土壤中铅（Pb）含量的分类研究，对5类不同污染程度的样本进行了分类试验。结果表明LIBSCNN方法可以实现土壤中Pb质量浓度等级的快速准确分类，准确度达到99%以上。相较于常用的化学方法，LIBS技术可以原位快速地对待测样品进行检测，样品预处理简单。因此LIBS CNN方法可以为后期土壤修复技术提供更加准确有效的数据，且节约大量的时间及成本，提高检测分辨率。

参考文献/References:

［1］尹文怡,刘玉柱,邱学军,等.激光诱导击穿光谱对四种香的快速检测［J］.光谱学与光谱分析,2018,38(9):2957.
［2］王彩虹,黄林,陈添兵,等.水田污染区稻壳与糙米中铬元素的LIBS分析可行性［J］.光谱学与光谱析,2017,37(11):3590.
［3］柯轲,吕勇,易灿灿.基于凸优化的激光诱导击穿光谱基线校正方法［J］.光谱学与光谱分析,2018,38(7):2256.
［4］杨晖,王彩虹,刘木华,等.样品物理方法前处理提高猪肉中Pb元素的LIBS分析精度研究［J］.光谱学与光谱分析,2017,37(8):2580.
［5］YU X J, LU H D, LIU Q Y. Deeplearningbased regression model and hyperspectral imaging for rapid detection of nitrogen concentration in oilseed rape (Brassica napus L.) leaf［J］. Chemometrics and Intelligent Laboratory Systems,2018,172:188.
［6］YU X J, LU H D, WU D. Development of deep learning method for predicting firmness and soluble solid content of postharvest Korla fragrant pear using Vis/NIR hyperspectral reflectance imaging［J］. Postharvest Biology and Technology,2018,141:39.
［7］YU X J, TANG L, WU X F, et al. Nondestructive freshness discriminating of shrimp using visible/nearinfrared hyperspectral imaging technique and deep learning algorithm［J］. Food Analytical Methods,2018,11:768.
［8］PENG J Y, SONG K L, ZHU H Y, et al. Fast detection of tobacco mosaic virus infected tobacco using laserinduced breakdown spectroscopy［J］. Scientific Reports,2017,7:44551.
［9］LIU X N, ZHANG Q, WU Z S, et al. Rapid elemental analysis and provenance study of blumea balsamifera DC using laserinduced breakdown spectroscopy［J］. Sensors,2015,15(1):642.
［10］孟德硕,赵南京,马明俊,等.基于激光诱导击穿光谱技术的土壤快速分类方法研究［J］.光谱学与光谱分析,2017,37(1):241.
［11］余克强,何勇,刘飞.基于激光诱导击穿光谱的土壤类型判别分析［J］.农业工程学报,2015,31(12):1.
［12］徐向君,王宪双,李昂泽,等.基于激光诱导击穿光谱的茶叶品种快速分类［J］.中国激光,2010,46(3):285.
［13］BURAKOV V S, RAIKOV S N, TARASENKO N V, et al. Development of a laserinduced breakdown spectroscopy method for soil and ecological analysis (review)［J］. Journal of Applied Spectroscopy,2010,77(5):595.
［14］GALIOVA M, KAISER J, NOVOTNY K, et al. Utilization of laser induced breakdown spectroscopy for investigation of the metal accumulation in vegetal tissues［J］. Spectrochimica Acta Part B,2007,62:1597.
［15］SENESI G S, AGLIO M D, GIACOMO A D, et al. Elemental composition analysis of plants and composts used for soil remediation by laserinduced breakdown spectroscopy［J］. CleanSoil, Air, Water,2014,42(6):1.
［16］GALIOVA M, KAISER J, NOVOTNY K, et al. Utilization of laserassisted analytical methods for monitoring of lead and nutrition elements distribution in fresh and dried capsicum annuum L. leaves［J］. Microscopy Research and Technique,2011,74:845.
［17］GARCIMUN～O M, PACE D M, BERTUCCELLI G. Laserinduced breakdown spectroscopy for quantitative analysis of copper in algae［J］. Optics & Laser Technology,2013,47:26.
［18］刘健,袁谦,吴广,等.卷积神经网络综述［J］.计算机时代,2018(11):5.
［19］张荣,李伟平,莫同.深度学习研究综述［J］.信息与控制,2018,47(4):385.
［20］孙志远,鲁成祥,史忠植,等.深度学习研究与进展［J］.计算机科学,2016,43(2):1.
［21］来文豪,周孟然,王亚,等.深度学习与激光诱导荧光在假酒识别中的应用［J］.激光与光电子学进展,2018,55(4):388.

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备注/Memo

备注/Memo:
收稿日期: 2019-03-13基金项目: 国家自然科学基金项目（61605173）；浙江省自然科学基金项目（LY16C130003）通信作者: 赵芸（1981—），女，浙江省杭州人，副教授，博士，主要从事信息工程与人工智能研究。
更新日期/Last Update: