Zhao Yun, Lamine Guindo Mahamed, Xu Xing, Sun Miao, Peng Jiyu, Liu Fei, He Yong
1 School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, China.
2 College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.
Appl Spectrosc. 2019 May;73(5):565-573. doi: 10.1177/0003702819826283. Epub 2019 Feb 7.
In this study, a method based on laser-induced breakdown spectroscopy (LIBS) was developed to detect soil contaminated with Pb. Different levels of Pb were added to soil samples in which tobacco was planted over a period of two to four weeks. Principal component analysis and deep learning with a deep belief network (DBN) were implemented to classify the LIBS data. The robustness of the method was verified through a comparison with the results of a support vector machine and partial least squares discriminant analysis. A confusion matrix of the different algorithms shows that the DBN achieved satisfactory classification performance on all samples of contaminated soil. In terms of classification, the proposed method performed better on samples contaminated for four weeks than on those contaminated for two weeks. The results show that LIBS can be used with deep learning for the detection of heavy metals in soil.
在本研究中，开发了一种基于激光诱导击穿光谱法（LIBS）的方法来检测受铅污染的土壤。在种植烟草的土壤样本中添加不同水平的铅，持续两到四周。采用主成分分析和深度信念网络（DBN）深度学习对LIBS数据进行分类。通过与支持向量机和偏最小二乘判别分析的结果进行比较，验证了该方法的稳健性。不同算法的混淆矩阵表明，DBN在所有污染土壤样本上都取得了令人满意的分类性能。在分类方面，所提出的方法对污染四周的样本比对污染两周的样本表现更好。结果表明，LIBS可与深度学习结合用于土壤中重金属的检测。
