Detection of K in soil using time-resolved laser-induced breakdown spectroscopy based on convolutional neural networks

文献类型： 外文期刊

第一作者： Lu, Chengxu

作者： Lu, Chengxu;Wang, Bo;Zhang, Junning;Niu, Kang;Yuan, Yanwei;Wang, Bo;Jiang, Xunpeng

作者机构：

关键词： quantitative detection; potassium (K); soil; time-resolved laser-induced breakdown spectroscopy (LIBS); convolutional neural networks (CNNs)

期刊名称：PLASMA SCIENCE & TECHNOLOGY （ 影响因子：1.567； 五年影响因子：1.281 ）

ISSN： 1009-0630

年卷期： 2019 年 21 卷 3 期

页码：

收录情况： SCI

摘要： 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.

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