Monitoring the Heavy Element of Cr in Agricultural Soils Using a Mobile Laser-Induced Breakdown Spectroscopy System with Support Vector Machine
Yan-Hong Gu1,2, Nan-Jing Zhao1**, Ming-Jun Ma1, De-Shuo Meng1, Yang Yu1, Yao Jia1, Li Fang1, Jian-Guo Liu1, Wen-Qing Liu1
1Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031 2University of Science and Technology of China, Hefei 230026
Abstract:Due to its complicated matrix effects, rapid quantitative analysis of chromium in agricultural soils is difficult without the concentration gradient samples by laser-induced breakdown spectroscopy. To improve the analysis speed and accuracy, two calibration models are built with the support vector machine method: one considering the whole spectra and the other based on the segmental spectra input. Considering the results of the multiple linear regression analysis, three segmental spectra are chosen as the input variables of the support vector regression (SVR) model. Compared with the results of the SVR model with the whole spectra input, the relative standard error of prediction is reduced from 3.18% to 2.61% and the running time is saved due to the decrease in the number of input variables, showing the robustness in rapid soil analysis without the concentration gradient samples.
. [J]. 中国物理快报, 2016, 33(08): 85201-085201.
Yan-Hong Gu, Nan-Jing Zhao, Ming-Jun Ma, De-Shuo Meng, Yang Yu, Yao Jia, Li Fang, Jian-Guo Liu, Wen-Qing Liu. Monitoring the Heavy Element of Cr in Agricultural Soils Using a Mobile Laser-Induced Breakdown Spectroscopy System with Support Vector Machine. Chin. Phys. Lett., 2016, 33(08): 85201-085201.
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