期刊
APPLIED SPECTROSCOPY
卷 73, 期 5, 页码 565-573出版社
SAGE PUBLICATIONS INC
DOI: 10.1177/0003702819826283
关键词
Deep learning; chemometrics; laser-induced breakdown spectroscopy; LIBS; principal component analysis; PCA
资金
- National Natural Science Foundation of China [61605173, 61403346]
- Natural Science Foundation of Zhejiang Province [LY16C130003]
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据