scholarly journals Laser-Induced Breakdown Spectroscopy for the Discrimination of Explosives Based on the ReliefF Algorithm and Support Vector Machines

2021 ◽  
Vol 9 ◽  
Author(s):  
Yu Zhao ◽  
Q. Q. Wang ◽  
Xutai Cui ◽  
Geer Teng ◽  
Kai Wei ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Classification Model ◽  
Laser Induced Breakdown Spectroscopy ◽  
Support Vector ◽  
Multivariate Statistical ◽  
Classification Rate ◽  
Breakdown Spectroscopy ◽  
Line Region ◽  
Vector Machines ◽  
Laser Induced Breakdown

Real-time explosive detectors must be developed to facilitate the rapid implementation of appropriate protective measures against terrorism. We report a simple yet efficient methodology to classify three explosives and three non-explosives by using laser-induced breakdown spectroscopy. However, the similarity existing among the spectral emissions collected from the explosives resulted in the difficulty of separating samples. We calculated the weights of lines by using the ReliefF algorithm and then selected six line regions that could be identified from the arrangement of weights to calculate the area of each line region. A multivariate statistical method involving support vector machines was followed for the construction of the classification model. Several models were constructed using full spectra, 13 lines, and 100 lines selected by the arrangement of weights and areas of the selected line regions. The highest correct classification rate of the model reached 100% by using the six line regions.

Classification of steel materials by laser-induced breakdown spectroscopy coupled with support vector machines

2014 ◽  
Vol 53 (4) ◽  
pp. 544 ◽  
Author(s):  
Long Liang ◽  
Tianlong Zhang ◽  
Kang Wang ◽  
Hongsheng Tang ◽  
Xiaofeng Yang ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Laser Induced Breakdown Spectroscopy ◽  
Support Vector ◽  
Breakdown Spectroscopy ◽  
Vector Machines ◽  
Laser Induced Breakdown

Interpreting support vector machines applied in laser-induced breakdown spectroscopy

2021 ◽  
pp. 339352
Author(s):  
Erik Képeš ◽  
Jakub Vrábel ◽  
Ondrej Adamovsky ◽  
Sára Střítežská ◽  
Pavlína Modlitbová ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Laser Induced Breakdown Spectroscopy ◽  
Support Vector ◽  
Breakdown Spectroscopy ◽  
Vector Machines ◽  
Laser Induced Breakdown

scholarly journals Analysis of Garnet by Laser-Induced Breakdown Spectroscopy—Two Practical Applications

Minerals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 705
Author(s):  
Peter A. Defnet ◽  
Michael A. Wise ◽  
Russell S. Harmon ◽  
Richard R. Hark ◽  
Keith Hilferding
Keyword(s):  
Optical Emission ◽  
Atomic Emission ◽  
Laser Induced Breakdown Spectroscopy ◽  
Support Vector ◽  
Success Rates ◽  
Practical Applications ◽  
Chemical Complexity ◽  
Geochemical Fingerprinting ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Laser-induced breakdown spectroscopy (LIBS) is a simple and straightforward technique of atomic emission spectroscopy that can provide multi-element detection and quantification in any material, in-situ and in real time because all elements emit in the 200–900 nm spectral range of the LIBS optical emission. This study evaluated two practical applications of LIBS—validation of labels assigned to garnets in museum collections and discrimination of LCT (lithium-cesium-tantalum) and NYF (niobium, yttrium and fluorine) pegmatites based on garnet geochemical fingerprinting, both of which could be implemented on site in a museum or field setting with a handheld LIBS analyzer. Major element compositions were determined using electron microprobe analysis for a suite of 208 garnets from 24 countries to determine garnet type. Both commercial laboratory and handheld analyzers were then used to acquire LIBS broadband spectra that were chemometrically processed by partial least squares discriminant analysis (PLSDA) and linear support vector machine classification (SVM). High attribution success rates (>98%) were obtained using PLSDA and SVM for the handheld data suggesting that LIBS could be used in a museum setting to assign garnet type quickly and accurately. LIBS also identifies changes in garnet composition associated with increasing mineral and chemical complexity of LCT and NYF pegmatites.

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.

scholarly journals Quantitative Determination of Cd in Soil Using Laser-Induced Breakdown Spectroscopy in Air and Ar Conditions

Molecules ◽  
2018 ◽  
Vol 23 (10) ◽  
pp. 2492 ◽  
Author(s):  
Xiaodan Liu ◽  
Fei Liu ◽  
Weihao Huang ◽  
Jiyu Peng ◽  
Tingting Shen ◽  
...  
Keyword(s):  
Heavy Metal ◽  
Least Squares ◽  
Three Dimensional ◽  
Accurate Method ◽  
Laser Induced Breakdown Spectroscopy ◽  
Quantitative Detection ◽  
Support Vector ◽  
Least Squares Regression ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Rapid detection of Cd content in soil is beneficial to the prevention of soil heavy metal pollution. In this study, we aimed at exploring the rapid quantitative detection ability of laser- induced breakdown spectroscopy (LIBS) under the conditions of air and Ar for Cd in soil, and finding a fast and accurate method for quantitative detection of heavy metal elements in soil. Spectral intensity of Cd and system performance under air and Ar conditions were analyzed and compared. The univariate model and multivariate models of partial least-squares regression (PLSR) and least-squares support vector machine (LS-SVM) of Cd under the air and Ar conditions were built, and the LS-SVM model under the Ar condition obtained the best performance. In addition, the principle of influence of Ar on LIBS detection was investigated by analyzing the three-dimensional profile of the ablation crater. The overall results indicated that LIBS combined with LS-SVM under the Ar condition could be a useful tool for the accurate quantitative detection of Cd in soil and could provide reference for environmental monitoring.

Sign in / Sign up

Export Citation Format

Share Document