Deep Learning Associated with Laser-Induced Breakdown Spectroscopy (LIBS) for the Prediction of Lead in Soil. Issue 5 (May 2019)

• Record Type: Journal Article
• Authors: Zhao, Yun; Lamine Guindo, Mahamed; Xu, Xing; Sun, Miao; Peng, Jiyu; Liu, Fei; He, Yong
• Is Part Of: Applied spectroscopy. Volume 73:Issue 5(2019)
• Page Start: 565
• Page End: 573
• Publication Date: 2019-05
• Subjects: Deep learning -- chemometrics -- laser-induced breakdown spectroscopy -- LIBS -- principal component analysis -- PCA; Spectrum analysis -- Periodicals; 543.505
• DOI: 10.1177/0003702819826283
• Languages: English
• ISSNs: 0003-7028

Abstract

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.