Multivariate Analysis of Laser-Induced Breakdown Spectroscopy Spectra of Soil Samples

Soil Science ◽  
2010 ◽  
Vol 175 (9) ◽  
pp. 447-452 ◽  
Author(s):  
Ningfang Yang ◽  
Neal S. Eash ◽  
Jaehoon Lee ◽  
Madhavi Z. Martin ◽  
Yong-Seon Zhang ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Soil Samples ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Deep Learning Associated with Laser-Induced Breakdown Spectroscopy (LIBS) for the Prediction of Lead in Soil

2019 ◽  
Vol 73 (5) ◽  
pp. 565-573 ◽  
Author(s):  
Yun Zhao ◽  
Mahamed Lamine Guindo ◽  
Xing Xu ◽  
Miao Sun ◽  
Jiyu Peng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Confusion Matrix ◽  
Principal Component ◽  
Classification Performance ◽  
Soil Samples ◽  
Laser Induced Breakdown Spectroscopy ◽  
Support Vector ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Different Levels

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.

Fast Determination of Copper Content in Tobacco (Nicotina tabacum L.) Leaves Using Laser-Induced Breakdown Spectroscopy with Univariate and Multivariate Analysis

2018 ◽  
Vol 61 (3) ◽  
pp. 821-829 ◽  
Author(s):  
Jiyu Peng ◽  
Lanhan Ye ◽  
Tingting Shen ◽  
Fei Liu ◽  
Kunlin Song ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Multivariate Analysis ◽  
Copper Content ◽  
Laser Induced Breakdown Spectroscopy ◽  
Fast Determination ◽  
Tobacco Leaves ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Determination Of Copper

Abstract. Fast and effective measures to determine heavy metals play an important role in ensuring food quality and safety. In this experiment, laser-induced breakdown spectroscopy (LIBS) was used to detect copper content (Cu) in tobacco ( L.) leaves. The experimental parameters for detection, including laser energy, delay time, and camera gate width, were optimized by response surface methodology (RSM). Univariate analysis and multivariate analysis, including partial least squares regression (PLSR) and extreme learning machine (ELM), were used to establish calibration models. In addition, different preprocessing methods were used to eliminate the signal variations and further improve the calibration performance, including baseline correction, background normalization, area normalization, and standard normal variate (SNV) normalization. The results showed that LIBS combined with both univariate and multivariate methods could be used to detect copper content in tobacco leaves. SNV and area normalization were efficient in dealing with signal variations and improving the calibration performance. The ELM model with SNV normalized variables in the spectral region of 324.02 to 325.98 nm achieved the best performance (R2 = 0.9552 and RMSE = 4.8416 mg kg-1 in the testing set). The results provide the first proof-of-principle data for fast determination of copper content in tobacco leaves. Keywords: Copper content, Laser-induced breakdown spectroscopy, Multivariate calibration, Response surface methodology, Tobacco leaves, Univariate calibration.

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