|
|
|
|
|
|
Comparative Analysis of Chemometrics Method on Heavy Metal Detection in Soil with Laser-Induced Breakdown Spectroscopy |
XIANG Li-rong, MA Zhi-hong, ZHAO Xin-yu, LIU Fei, HE Yong, FENG Lei* |
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China |
|
|
Abstract A large number of farm lands are contaminated by heavy metals in the process of industrialization and urbanization. Precise detection of heavy metals in soil offers valid reference for prevention and recovery of heavy metals in the field. In this research, Laser induced breakdown spectroscopy (LIBS) and chemometrics methods were employed to conduct quantitative analysis of heavy metals Pb and Cd in soil. Based on the pollution extent, soil samples with 15 concentration gradients of Pb and Cd were manually made up. Then, the LIBS emission lines of all soil samples were collected firstly. In order to eliminate errors and noise of spectral data, preprocessing methods called removal of abnormal data and normalization were used. Then, characteristic lines and spectral regions of Pb and Cd were determined based on our LIBS spectra and Atomic Spectra Database (ASD) of National Institute of Standards and Technology (NIST). Quantity regression models based on multiple linear regression (MLR), partial least squares regression (PLSR), least squares support vector machine (LS-SVM) and back propagation-artificial neural network (BP-ANN) were set up and their results were compared. As a result, models based on non-linear methods (LS-SVM and BP-ANN) offered a promising results than the linear methods of MLR and PLSR. The probable reason was that non-linear methods had an advantage to deal with matrix effects of soil automatically. The results indicated that LIBS coupled with multiple chemometrics methods provided a brand-new analysis approach for heavy metals accurate detection in soil and it could be considered as an effective theoretical foundation of making protection and recovery decision for soil contaminated by heavy metals.
|
Received: 2016-07-13
Accepted: 2016-11-22
|
|
Corresponding Authors:
FENG Lei
E-mail: Lfeng@zju.edu.cn
|
|
[1] DU Chuang, GAO Xun, SHAO Yan, et al(杜 闯,高 勋,邵 妍,等). Acta Physica Sinica(物理学报), 2013, 62(4): 221.
[2] ZHANG Xiao-min, ZHANG Xiu-ying, ZHONG Tai-yang, et al(张小敏,张秀英,钟太洋,等). Environmental Science(环境科学), 2014, 35(2): 692.
[3] Khan K, Lu Y, Khan H, et al. Food and Chemical Toxicology, 2013, 58(7): 449.
[4] GU Jun-ping, HU Jing, ZHOU Lang-jun, et al(古君平,胡 静,周郎君,等). Journal of Instrumental Analysis(分析测试学报), 2015, 1: 111.
[5] Kim G, Kwak J, Kim K R, et al. Journal of Hazardous Materials, 2013, 263: 754.
[6] LIU Jia, GAO Xun, DUAN Hua-hua, et al(刘 佳,高 勋,段花花,等). Laser Journal(激光杂志), 2012, 1: 7.
[7] Kwak J, Kim K W, Park M, et al. Environmental Technology, 2012, 33(16-18): 2177.
[8] Ferreira E C, Milori D M B P, Ferreira E J, et al. Spectrochimica Acta Part B, 2008, 63(10): 1216.
[9] Barbafieri M, Pini R, Ciucci A, et al. Chemistry and Ecology, 2011, 27(27): 161.
[10] Albergaria J T, Martins F G, Alvim-Ferraz M C M, et al. Water Air & Soil Pollution, 2014, 225(8): 1.
[11] Panagou E Z, Mohareb F R, Argyri A A, et al. Food Microbiology, 2011, 28(4): 782.
[12] Balabin R M, Lomakina E I. Analyst, 2011, 136(8): 1703.
[13] Piotrowski A P, Osuch M, Napiorkowski M J, et al. Computers & Geosciences, 2014, 64(3): 136.
[14] Wang H S, Wang Y N, Wang Y C. Expert Systems with Applications, 2013, 40(2): 418.
[15] HE Xiu-wen, CHEN Tian-bing, YAO Ming-yin, et al(何秀文, 陈添兵, 姚明印,等). Chinese Journal of Analytical Chemistry(化学分析), 2016, 44(1): 68.
[16] Zhan X, Xiao L, Xu G, et al. Environmental Pollution, 2013, 179(8): 294. |
[1] |
XU Tian1, 2, LI Jing1, 2, LIU Zhen-hua1, 2*. Remote Sensing Inversion of Soil Manganese in Nanchuan District, Chongqing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 69-75. |
[2] |
LIANG Ye-heng1, DENG Ru-ru1, 2*, LIANG Yu-jie1, LIU Yong-ming3, WU Yi4, YUAN Yu-heng5, AI Xian-jun6. Spectral Characteristics of Sediment Reflectance Under the Background of Heavy Metal Polluted Water and Analysis of Its Contribution to
Water-Leaving Reflectance[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 111-117. |
[3] |
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. |
[4] |
LI Hu1, ZHONG Yun1, 2, FENG Ya-ting1, LIN Zhen1, ZHU Shi-jiang1, 2*. Multi-Vegetation Index Soil Moisture Inversion Model Based on UAV
Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 207-214. |
[5] |
HAN Xue1, 2, LIU Hai1, 2, LIU Jia-wei3, WU Ming-kai1, 2*. Rapid Identification of Inorganic Elements in Understory Soils in
Different Regions of Guizhou Province by X-Ray
Fluorescence Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 225-229. |
[6] |
MENG Shan1, 2, LI Xin-guo1, 2*. Estimation of Surface Soil Organic Carbon Content in Lakeside Oasis Based on Hyperspectral Wavelet Energy Feature Vector[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3853-3861. |
[7] |
LI Qi-chen1, 2, LI Min-zan1, 2*, YANG Wei2, 3, SUN Hong2, 3, ZHANG Yao1, 3. Quantitative Analysis of Water-Soluble Phosphorous Based on Raman
Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3871-3876. |
[8] |
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. |
[9] |
CHENG Hui-zhu1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, MA Qian1, 2, ZHAO Yan-chun1, 2. Genetic Algorithm Optimized BP Neural Network for Quantitative
Analysis of Soil Heavy Metals in XRF[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3742-3746. |
[10] |
LIU Hao-dong1, 2, JIANG Xi-quan1, 2, NIU Hao1, 2, LIU Yu-bo1, LI Hui2, LIU Yuan2, Wei Zhang2, LI Lu-yan1, CHEN Ting1,ZHAO Yan-jie1*,NI Jia-sheng2*. Quantitative Analysis of Ethanol Based on Laser Raman Spectroscopy Normalization Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3820-3825. |
[11] |
LIN Hong-jian1, ZHAI Juan1*, LAI Wan-chang1, ZENG Chen-hao1, 2, ZHAO Zi-qi1, SHI Jie1, ZHOU Jin-ge1. Determination of Mn, Co, Ni in Ternary Cathode Materials With
Homologous Correction EDXRF Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3436-3444. |
[12] |
HUANG Li, MA Rui-jun*, CHEN Yu*, CAI Xiang, YAN Zhen-feng, TANG Hao, LI Yan-fen. Experimental Study on Rapid Detection of Various Organophosphorus Pesticides in Water by UV-Vis Spectroscopy and Parallel Factor Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3452-3460. |
[13] |
XIE Peng, WANG Zheng-hai*, XIAO Bei, CAO Hai-ling, HUANG Yi, SU Wen-lin. Hyperspectral Quantitative Inversion of Soil Selenium Content Based on sCARS-PSO-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3599-3606. |
[14] |
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. |
[15] |
LI Zhong-bing1, 2, JIANG Chuan-dong2, LIANG Hai-bo3, DUAN Hong-ming2, PANG Wei2. Rough and Fine Selection Strategy Binary Gray Wolf Optimization
Algorithm for Infrared Spectral Feature Selection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3067-3074. |
|
|
|
|