|
|
|
|
|
|
| Study on Improving the Stability of Heavy Metal Cu in Soil by Image Optimization |
| LIN Xiao-mei, TAO Si-yu, LIN Jing-jun*, HUANG Yu-tao, CHE Chang-jin, SUN Hao-ran |
| School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China |
|
|
|
|
Abstract In order to improve the stability of the characteristic spectral lines of laser-induced breakdown spectra of heavy metals in soil and the accuracy of soil quantitative analysis, the image optimization technology and laser-induced plasma technology were combined to analyze element Cu in soil. The two characteristics lines, Cu Ⅰ 324.75 nm and Cu Ⅰ 327.40 nm, were compared and analyzed by experiments. Finally, Cu Ⅰ 324.75 nm was selected as the analytical spectral line. The method of wavelet transform was used to denoise the spectrum, which eliminates the influence of matrix effect on the results and improves the stability of the spectral. Then the real-time acquisition of plasma images with different delays was carried out. The influence of delays on spot area and spectral intensity was analyzed, and the optimal delay was determined to be 900 ns. The RSD of different concentrations with the image optimization model was compared with that without optimization at the optimal delay time and the same energy. The optimal plasma image was selected by the image optimization model to calculate the RSD. It was found that RSD with improved greatly. The RSD of each concentration without optimization was 5.39%, 6.22%, 7.56%, 8.42% and 9.63%, respectively. The RSD with optimization was 3.24%, 4.47%, 5.32%, 6.13% and 7.21%, respectively. The method of image optimization effectively suppresses the continuous background radiation and improves the stability and repeatability of the spectrum. Compared with the data without image optimization, the RSD with optimization decreased by 2.15%, 1.75%, 2.24%, 2.29% and 2.42%, respectively. The date proved that the stability of detecting the content of copper in soil was greatly improved. Finally, the standard internal method was used to analyze the heavy metals in soil quantitatvely. Compared with the non-optimal condition, the accuracy and stability of the calibration model under the optimal condition were improved. The R2 increased from 0.978 to 0.995. It can be seen from the data above that image optimization technology greatly improves the spectral stability. The image optimization technology can greatly improve the quantitative analysis ability in soil heavy metal detection using LIBS.
|
|
Received: 2019-09-03
Accepted: 2020-01-15
|
|
|
|
Corresponding Authors:
LIN Jing-jun
E-mail: 1124270941@qq.com
|
|
[1] WANG Jian-wei, ZHANG Na-zhen, HOU Ke-yong, et al(王建伟，张娜珍，侯可勇，等）. Progress in Chemistry(化学进展)，2008，20（7）: 1165.
[2] HE Tian-ye, ZHANG Ying-hong, HU Zi-wen, et al(何恬叶，张颖红，胡子文，等). Chinese Journal of Analysis Laboratory(分析实验室)，2018，37（1）：84.
[3] REN Dong, SHEN Jun, REN Shun, et al(任 东，沈 俊，任 顺，等). Spectroscopy and Spectroscopy Analysis(光谱学与光谱分析)，2018，38（12）：3934.
[4] MENG De-shuo, ZHAO Nan-jing，LIU Wen-qing, et al（孟德硕，赵南京，刘文清, 等）. Chinese Journal of Lasers(中国激光), 2014, 41(7): 0715002.
[5] Ramli, Muliadi，Khumaeni, Ali，et al. Spectrochimica Acta Part B: Atomic Spectroscopy, 2017，132(8)：17.
[6] CHEN Tian-bing, YAO Ming-yin, LIU Mu-hua, et al(陈添兵，姚明印，刘木华，等). Applied Laser(应用激光)，2011，31（12）：478.
[7] Zhang Guiyin, Song Huan, Liu Yang, et al. Optik, 2018, 3: 87.
[8] YU Yang, ZHAO Nan-jing, MENG De-shuo, et al(余 洋，赵南京，孟德硕, 等). Chinese Journal of Lasers(中国激光), 2018, 45(8): 0811001.
[9] Fu H, Dong F, Ni Z. Applied Spectroscopy, 2016, 70(3): 405.
[10] Lepore K H, Fassett C I, Breves E A, et al. Applied Spectroscopy, 2017, 71(4): 1.
[11] Pan C, Du X, An N, et al. Applied Spectroscopy, 2016, 70(4): 702. |
| [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] |
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. |
| [3] |
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. |
| [4] |
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. |
| [5] |
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. |
| [6] |
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. |
| [7] |
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. |
| [8] |
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. |
| [9] |
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. |
| [10] |
AN Bai-song1, 2, WANG Xue-mei1, 2*, HUANG Xiao-yu1, 2, KAWUQIATI Bai-shan1, 2. Hyperspectral Estimation of Soil Lead Content Based on Random Frog Band Selection Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3302-3309. |
| [11] |
DENG Yun1, 2, NIU Zhao-wen1, 2, FENG Qi-yao1, 2, WANG Yu1, 2*. A Novel Hyperspectral Prediction Model of Organic Matter in Red Soil Based on Improved Temporal Convolutional Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2942-2951. |
| [12] |
CAI Hai-hui1, ZHOU Ling2, SHI Zhou3, JI Wen-jun4, LUO De-fang1, PENG Jie1, FENG Chun-hui5*. Hyperspectral Inversion of Soil Organic Matter in Jujube Orchard
in Southern Xinjiang Using CARS-BPNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2568-2573. |
| [13] |
XIA Chen-zhen1, 2, 3, JIANG Yan-yan4, ZHANG Xing-yu1, 2, 3, SHA Ye5, CUI Shuai1, 2, 3, MI Guo-hua5, GAO Qiang1, 2, 3, ZHANG Yue1, 2, 3*. Estimation of Soil Organic Matter in Maize Field of Black Soil Area Based on UAV Hyperspectral Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2617-2626. |
| [14] |
ZHANG Zi-hao1, GUO Fei3, 4, WU Kun-ze1, YANG Xin-yu2, XU Zhen1*. Performance Evaluation of the Deep Forest 2021 (DF21) Model in
Retrieving Soil Cadmium Concentration Using Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2638-2643. |
| [15] |
HU Meng-ying1, 2, ZHANG Peng-peng1, 2, LIU Bin1, 2, DU Xue-miao1, 2, ZHANG Ling-huo1, 2, XU Jin-li1, 2*, BAI Jin-feng1, 2. Determination of Si, Al, Fe, K in Soil by High Pressure Pelletised Sample and Laser-Induced Breakdown Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2174-2180. |
|
|
|
|
