激光与光电子学进展, 2018, 55 (9): 093005, 网络出版: 2018-09-08   

基于生物地理学优化算法的水体重金属激光诱导击穿光谱定量分析 下载: 607次

Quantitative Analysis of Laser-Induced Breakdown Spectroscopy of Heavy Metals in Water Based on Biogeography-Based Optimization Algorithm
作者单位
1 西安电子科技大学物理与光电工程学院, 陕西 西安 710071
2 深圳大学光电工程学院, 广东 深圳 518060
摘要
激光诱导击穿光谱(LIBS)技术是一种基于原子发射光谱和等离子体发射光谱的物质成分分析技术。利用LIBS技术对水中的Pb污染进行检测, 选择Pb元素的最强峰405.8 nm作为分析线, 以Si(390.6 nm)为内标元素, 经线性拟合得到Pb的检出限为7.40×10-6。建立了基于生物地理学优化(BBO)算法的定量分析模型, 利用该模型分别测定了不同Pb元素浓度的35份样品的LIBS谱线, 其中的30组数据被用来训练BBO定量分析模型, 剩余的5组数据被作为测试集来评估模型的分析能力。结果表明:在利用BBO算法模型对水体中Pb浓度进行预测时, 模型的相对标准偏差(RSD)及平均绝对百分比误差(MAPE)指标都相当优异。
Abstract
Laser-induced breakdown spectroscopy (LIBS) technology is an element analysis technology based on atomic emission spectroscopy and plasma emission spectroscopy. In this study, LIBS is used to detect the lead (Pb) concentrations in water. The strongest spectral line of Pb 405.8 nm is selected as the analytical line and Si 390.6 nm is used as internal standard element. The detection limit of Pb obtained by linear fitting is determined to be 7.40×10-6 . A quantitative analysis model based on biogeography-based optimization (BBO) algorithm is established. Using this model, we establish the LIBS spectra of 35 samples with different Pb concentrations. Among them, 30 sets of data are used to train the BBO quantitative analysis model, and the remaining 5 sets of data are used as test sets to evaluate the analytical ability of the model. The results show that the relative standard deviation (RSD) and the mean absolute percentage error (MAPE) of the model are quite good when using the BBO algorithm model to predict the Pb concentration in water.
参考文献

[1] Bauer A J, Buckley S G. Novel applications of laser-induced breakdown spectroscopy[J]. Applied Spectroscopy, 2017, 71(4): 553-566.

[2] 陈娜, 刘尧香, 杜盛喆, 等. 纳秒、飞秒激光诱导击穿光谱技术的应用研究进展[J]. 激光与光电子学进展, 2016, 53(5): 050003.

    Chen N, Liu Y X, Du S Z, et al. Research progress in applications of nanosecond and femtosecond laser-induced breakdown spectroscopy[J]. Laser & Optoelectronics Progress, 2016, 53(5): 050003.

[3] Martin M Z, Mayes M A, Heal K R, et al. Investigation of laser-induced breakdown spectroscopy and multivariate analysis for differentiating inorganic and organic C in a variety of soils[J]. Spectrochimica Acta Part B: Atomic Spectroscopy, 2013, 87(9): 100-107.

[4] Cremers D A, Ferris M J. Extending the applicability of laser-induced breakdown spectroscopy for total soil carbon measurement[J]. Soil Science Society of America Journal, 2003, 67(5): 1616-1619.

[5] El Haddad J, Villot-Kadri M, Ismal A, et al. Artificial neural network for on-site quantitative analysis of soils using laser induced breakdown spectroscopy[J]. Spectrochimica Acta Part B: Atomic Spectroscopy, 2013, 79/80(3): 51-57.

[6] 胡杨, 李子涵, 吕涛. 激光诱导击穿光谱结合人工神经网络测定地质标样中的铁含量[J]. 激光与光电子学进展, 2017, 54(5): 053003.

    Hu Y, Li Z H, Lü T. Quantitative measurement of iron content in geological standard samples by laser-induced breakdown spectroscopy combined with artificial neural network[J]. Laser & Optoelectronics Progress, 2017, 54(5): 053003.

[7] Kong H Y, Sun L X, Hu J T, et al. Quantitative analysis of steels using PLS with three data reduction methods based on LIBS[J]. Advanced Materials Research, 2014, 997: 578-582.

[8] Yang G, Qiao S J, Chen P F, et al. Rock and soil classification using PLS-DA and SVM combined with a laser-induced breakdown spectroscopy library[J]. Plasma Science and Technology, 2015, 17(8): 656-663.

[9] Simon D. Biogeography-based optimization[J]. IEEE Transactions on Evolutionary Computation, 2008, 12(6): 702-713.

[10] 冯思玲, 朱清新. 生物地理学优化算法研究进展[J]. 运筹与模糊学, 2014, 4(2): 25-34.

    Feng S L, Zhu Q X. Research process of biogeography-based optimization[J]. Operations Research and Fuzziology, 2014, 4(2): 25-34.

[11] 张国辉, 聂黎, 张利平. 生物地理学优化算法理论及其应用研究综述[J]. 计算机工程与应用, 2015, 51(3): 12-17.

    Zhang G H, Nie L, Zhang L P. Review on biogeography-based optimization algorithm and applications[J]. Computer Engineering and Applications, 2015, 51(3): 12-17.

[12] Wesche T A, Goertler C M, Hubert W A. Modified habitat suitability index model for brown trout in southeastern Wyoming[J]. North American Journal of Fisheries Management, 1987, 7(2): 232-237.

[13] Mirjalili S, Mirjalili S M, Lewis A. Let a biogeography-based optimizer train yourmulti-layer perceptron[J]. Information Sciences, 2014, 269(8): 188-209.

[14] 王娟, 吴宪祥, 曹艳玲. 基于差分进化生物地理学优化的多层感知器训练方法[J]. 计算机应用研究, 2017, 34(3): 693-696.

    Wang J, Wu X X, Cao Y L. Multi-layer perceptron using hybrid differential evolution and biogeography-based optimization[J]. Application Research of Computers, 2017, 34(3): 693-696.

[15] 沈沁梅, 周卫东, 李科学. 基于遗传神经网络的激光诱导击穿光谱元素定量分析技术[J]. 中国激光, 2011, 38(3): 0315001.

    Shen Q M, Zhou W D, Li K X. Quantative elemental analysis using laser induced breakdown spectroscopy and neuro-genetic approach[J]. Chinese Journal of Lasers, 2011, 38(3): 0315001.

[16] 修俊山, 侯华明, 钟石磊, 等. 以滤纸为基质利用LIBS定量分析水溶液中铅元素[J]. 中国激光, 2011, 38(8): 0815003.

    Xiu J S, Hou H M, Zhong S L, et al. Quantitative determination of heavy metal element Pb in aqueous solutions by laser-induced breakdown spectroscopy using paper slice substrates[J]. Chinese Journal of Lasers, 2011, 38(8): 0815003.

[17] 张纪会, 高齐圣, 徐心和. 自适应蚁群算法[J]. 控制理论与应用, 2000, 17(1): 1-3.

    Zhang J H, Gao Q S, Xu X H. A self-adaptive ant colony algorithm[J]. Control Theory and Applications, 2000, 17(1): 1-3.

[18] Yaseen S G, Al-Slamy N M A. Ant colony optimization[J]. International Journal of Computer Science and Network Security, 2008, 8(6): 351-357.

[19] Diaz Pace D M, D′Angelo C A, Bertuccelli D, et al. Analysis of heavy metals in liquids using laser induced breakdown spectroscopy by liquid-to-solid matrix conversion[J]. Spectrochimica Acta Part B: Atomic Spectroscopy, 2006, 61(8): 929-933.

刘立新, 孙罗庚, 李梦珠, 祝铭. 基于生物地理学优化算法的水体重金属激光诱导击穿光谱定量分析[J]. 激光与光电子学进展, 2018, 55(9): 093005. Liu Lixin, Sun Luogeng, Li Mengzhu, Zhu Ming. Quantitative Analysis of Laser-Induced Breakdown Spectroscopy of Heavy Metals in Water Based on Biogeography-Based Optimization Algorithm[J]. Laser & Optoelectronics Progress, 2018, 55(9): 093005.

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