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 anal...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.展开更多
Detection of oil pollution in soil has been carried out using laser-induced breakdown spectroscopy（LIBS）. A pulsed neodymium-doped yttrium aluminum garnet（Nd：YAG） laser（1,064 nm, 8 ns, 200 mJ） was focused onto ...Detection of oil pollution in soil has been carried out using laser-induced breakdown spectroscopy（LIBS）. A pulsed neodymium-doped yttrium aluminum garnet（Nd：YAG） laser（1,064 nm, 8 ns, 200 mJ） was focused onto pelletized soil samples. Emission spectra were obtained from oil-contaminated soil and clean soil. The contaminated soil had almost the same spectrum profile as the clean soil and contained the same major and minor elements. However, a C–H molecular band was clearly detected in the oil-contaminated soil, while no C–H band was detected in the clean soil. Linear calibration curve of the C–H molecular band was successfully made by using a soil sample containing various concentrations of oil. The limit of detection of the C–H band in the soil sample was 0.001 mL/g. Furthermore, the emission spectrum of the contaminated soil clearly displayed titanium（Ti） lines, which were not detected in the clean soil. The existence of the C–H band and Ti lines in oil-contaminated soil can be used to clearly distinguish contaminated soil from clean soil. For comparison, the emission spectra of contaminated and clean soil were also obtained using scanning electron microscope-energy dispersive X-ray（SEM/EDX） spectroscopy,showing that the spectra obtained using LIBS are much better than using SEM/EDX, as indicated by the signal to noise ratio（S/N ratio）.展开更多
One of the technical bottlenecks of traditional laser-induced breakdown spectroscopy(LIBS) is the difficulty in quantitative detection caused by the matrix effect. To troubleshoot this problem,this paper investigated ...One of the technical bottlenecks of traditional laser-induced breakdown spectroscopy(LIBS) is the difficulty in quantitative detection caused by the matrix effect. To troubleshoot this problem,this paper investigated a combination of time-resolved LIBS and convolutional neural networks(CNNs) to improve K determination in soil. The time-resolved LIBS contained the information of both wavelength and time dimension. The spectra of wavelength dimension showed the characteristic emission lines of elements, and those of time dimension presented the plasma decay trend. The one-dimensional data of LIBS intensity from the emission line at 766.49 nm were extracted and correlated with the K concentration, showing a poor correlation of R_c^2?=?0.0967, which is caused by the matrix effect of heterogeneous soil. For the wavelength dimension, the two-dimensional data of traditional integrated LIBS were extracted and analyzed by an artificial neural network(ANN), showing R_v^2?=?0.6318 and the root mean square error of validation(RMSEV)?=?0.6234. For the time dimension, the two-dimensional data of time-decay LIBS were extracted and analyzed by ANN, showing R_v^2?=?0.7366 and RMSEV?=?0.7855.These higher determination coefficients reveal that both the non-K emission lines of wavelength dimension and the spectral decay of time dimension could assist in quantitative detection of K.However, due to limited calibration samples, the two-dimensional models presented over-fitting.The three-dimensional data of time-resolved LIBS were analyzed by CNNs, which extracted and integrated the information of both the wavelength and time dimension, showing the R_v^2?=?0.9968 and RMSEV?=?0.0785. CNN analysis of time-resolved LIBS is capable of improving the determination of K in soil.展开更多
基金Supported by the National High-Technology Research and Development Program of China under Grant Nos 2014AA06A513 and 2013AA065502the National Natural Science Foundation of China under Grant No 61378041the Anhui Province Outstanding Youth Science Fund of China under Grant No 1508085JGD02
文摘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.
基金financially supported by Diponegoro University,Semarang,Indonesia (31419/UN7.5.1/PG/2015 and 573-18/UN7.5.1/PG/2016)
文摘Detection of oil pollution in soil has been carried out using laser-induced breakdown spectroscopy（LIBS）. A pulsed neodymium-doped yttrium aluminum garnet（Nd：YAG） laser（1,064 nm, 8 ns, 200 mJ） was focused onto pelletized soil samples. Emission spectra were obtained from oil-contaminated soil and clean soil. The contaminated soil had almost the same spectrum profile as the clean soil and contained the same major and minor elements. However, a C–H molecular band was clearly detected in the oil-contaminated soil, while no C–H band was detected in the clean soil. Linear calibration curve of the C–H molecular band was successfully made by using a soil sample containing various concentrations of oil. The limit of detection of the C–H band in the soil sample was 0.001 mL/g. Furthermore, the emission spectrum of the contaminated soil clearly displayed titanium（Ti） lines, which were not detected in the clean soil. The existence of the C–H band and Ti lines in oil-contaminated soil can be used to clearly distinguish contaminated soil from clean soil. For comparison, the emission spectra of contaminated and clean soil were also obtained using scanning electron microscope-energy dispersive X-ray（SEM/EDX） spectroscopy,showing that the spectra obtained using LIBS are much better than using SEM/EDX, as indicated by the signal to noise ratio（S/N ratio）.
基金supported by National Natural Science Foundation of China (Grant No. 61505253)National Key Research and Development Plan of China (Project No. 2016YFD0200601)
文摘One of the technical bottlenecks of traditional laser-induced breakdown spectroscopy(LIBS) is the difficulty in quantitative detection caused by the matrix effect. To troubleshoot this problem,this paper investigated a combination of time-resolved LIBS and convolutional neural networks(CNNs) to improve K determination in soil. The time-resolved LIBS contained the information of both wavelength and time dimension. The spectra of wavelength dimension showed the characteristic emission lines of elements, and those of time dimension presented the plasma decay trend. The one-dimensional data of LIBS intensity from the emission line at 766.49 nm were extracted and correlated with the K concentration, showing a poor correlation of R_c^2?=?0.0967, which is caused by the matrix effect of heterogeneous soil. For the wavelength dimension, the two-dimensional data of traditional integrated LIBS were extracted and analyzed by an artificial neural network(ANN), showing R_v^2?=?0.6318 and the root mean square error of validation(RMSEV)?=?0.6234. For the time dimension, the two-dimensional data of time-decay LIBS were extracted and analyzed by ANN, showing R_v^2?=?0.7366 and RMSEV?=?0.7855.These higher determination coefficients reveal that both the non-K emission lines of wavelength dimension and the spectral decay of time dimension could assist in quantitative detection of K.However, due to limited calibration samples, the two-dimensional models presented over-fitting.The three-dimensional data of time-resolved LIBS were analyzed by CNNs, which extracted and integrated the information of both the wavelength and time dimension, showing the R_v^2?=?0.9968 and RMSEV?=?0.0785. CNN analysis of time-resolved LIBS is capable of improving the determination of K in soil.
