1. Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China
2. Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China
3. Jiangsu Engineering Research Center on Meteorological Energy Using and Control, Nanjing University of Information Science & Technology, Nanjing 210044, China
Abstract：The detection of heavy metals in soil is one of the emphases of environmental protection. This paper aims to establish a fast and quantitative method for the determination of heavy metal elements in soil, based on LIBS and combined with the PLS method. We used PLS model to quantitatively analyze and predict the contents of Cu and Ni elements in oil-contaminated samples. On this basis, the variables of the full spectrum were screened by combining the Interval method and the Backward Interval method, which formed the Interval Partial Least Square (iPLS) and the Backward Interval Partial Least Square (BiPLS). The experimental results showed that the BiPLS method retained more spectral information after removing the interference information, and obtained better-predicted results than PLS and IPLS. The R2P and RMSEP of the predicted results of the test set for the copper element are 0.944 9 and 0.036 3, respectively, and the RPD reached 3.0. Those of the predicted results of the test set for nickel element are 0.933 7 and 0.041 4, respectively, and the RPD reached 2.6. Compared with the PLS and iPLS methods, the prediction results of the BiPLS method of the two elements were significantly optimized, the predictive ability was significantly improved, and the accuracy was much better. Therefore, In the analysis of heavy metal elements in oil-contaminated soil by LIBS technique, BiPLS is more suitable than iPLS and PLS for screening the feature variables that contribute greatly to the quantitative analysis of Cu and Ni elements, so as to improve the prediction effect. This method will promote the application of LIBS technology to the online evaluation of soil quality.
Key words：Oil-contaminated soil; Heavy metal detection; LIBS; Partial least squares
朱绍农，丁 宇，陈雨娟，邓 凡，陈非凡，严 飞. LIBS与变量选择PLS结合的含油土壤中Cu，Ni定量分析[J]. 光谱学与光谱分析, 2020, 40(12): 3812-3817.
ZHU Shao-nong, DING Yu，CHEN Yu-juan, DENG Fan, CHEN Fei-fan, YAN Fei. Quantitative Analysis of Cu and Ni in Oil-Contaminated Soil by LIBS Combined With Variable Selection Method and PLS. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(12): 3812-3817.
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