Comparison of Chemometrics Method on K Detection in Soil Using Laser Induced Breakdown Spectroscopy

文献类型： 外文期刊

第一作者： Lu, Chengxu

作者： Lu, Chengxu;Yuan, Yanwei;Niu, Kang;Qi, Yannan;Zhang, Junning;Jiang, Xunpeng

作者机构：

关键词： K; Soil; Laser Induced Breakdown Spectroscopy; Artificial Neural Networks; Partial Least Squares

期刊名称：FIFTH SYMPOSIUM ON NOVEL OPTOELECTRONIC DETECTION TECHNOLOGY AND APPLICATION

ISSN： 0277-786X

年卷期： 2019 年 11023 卷

页码：

收录情况： SCI

摘要： Potassium detection in the soil is of significant importance for agricultural industry. In this paper, chemometrics methods of artificial neural networks (ANN) and partial least squares (PLS) were comparatively used to detect K in the soil with laser induced breakdown spectroscopy (LIBS). In total, 12 certified reference soils and 17 simulated soil samples with the K concentration of 0.1 similar to 3.3% were prepared. LIBS spectra at the wavelength of 723.62 similar to 808.24 nm were collected, and then analyzed with ANN and PLS method. The PLS model presented the result of R-val(2)= 0.92 and RMSEV=0.26, the ANN model presented the result of R-val(2)=0.82 and RMSEV=0.40. ANN model showed under-fitting and the PLS model performed a better RPD than that of ANN. This demonstrated that the linear PLS model is capable to determinate K concentration in the soil using LIBS.

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