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.

Retracted Article: Novel techniques for enhancing the performance of support vector regression chemo-metric in quantitative analysis of LIBS spectra

2017 ◽  
Author(s):  
Taoreed Olakunle Owolabi ◽  
Mohammed Gondal
Keyword(s):  
Quantitative Analysis ◽  
Emission Spectroscopy ◽  
Atomic Emission ◽  
Small Sample ◽  
Laser Induced Breakdown Spectroscopy ◽  
Support Vector ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Retracted Article ◽  
No Sample Preparation

Laser induced breakdown spectroscopy (LIBS) is an atomic emission spectroscopy through which elemental compositions of materials can be determined with little or no sample preparation. Small sample requirement as well...

Characterization of Amethysts from Sukamara, Central Kalimantan, Using Laser-Induced Breakdown Spectroscopy (LIBS)

2020 ◽  
Vol 1 (2) ◽  
pp. 5-8
Author(s):  
Komang Gde Suastika, Heri Suyanto, Gunarjo, Sadiana, Darmaji
Keyword(s):  
Emission Intensity ◽  
Laser Energy ◽  
Emission Spectra ◽  
Atomic Emission ◽  
Laser Induced Breakdown Spectroscopy ◽  
Chemical Formula ◽  
Central Kalimantan ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Abstract - Laser-Induced Breakdown Spectroscopy (LIBS) is one method of atomic emission spectroscopy using laser ablation as an energy source. This method is used to characterize the type of amethysts that originally come from Sukamara, Central Kalimantan. The result of amethyst characterization can be used as a reference for claiming the natural wealth of the amethyst. The amethyst samples are directly taken from the amethyst mining field in the District Gem Amethyst and consist of four color variations: white, black, yellow, and purple. These samples were analyzed by LIBS, using laser energy of 120 mJ, delay time detection of 2 μs and accumulation of 3, with and without cleaning. The purpose of this study is to determine emission spectra characteristics, contained elements, and physical characteristics of each amethyst sample. The spectra show that the amethyst samples contain some elements such as Al, Ca, K, Fe, Gd, Ba, Si, Be, H, O, N, Cl and Pu with various emission intensities. The value of emission intensity corresponds to concentration of element in the sample. Hence, the characteristics of the amethysts are based on their concentration value. The element with the highest concentration in all samples is Si, which is related to the chemical formula of SiO2. The element with the lowest concentration in all samples is Ca that is found in black and yellow amethysts. The emission intensity of Fe element can distinguish between white, purple, and yellow amethyst. If Fe emission intensity is very low, it indicates yellow sample. Thus, we may conclude that LIBS is a method that can be used to characterize the amethyst samples.Key words: amethyst, impurity, laser-induced, breakdown spectroscopy, characteristic, gemstones

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.

Optimization of distances between the target surface and focal point on spatially confined laser-induced breakdown spectroscopy with a cylindrical cavity

2017 ◽  
Vol 32 (2) ◽  
pp. 367-372 ◽  
Author(s):  
Jin Guo ◽  
Junfeng Shao ◽  
Tingfeng Wang ◽  
Changbin Zheng ◽  
Anmin Chen ◽  
...  
Keyword(s):  
Emission Spectroscopy ◽  
Optical Emission Spectroscopy ◽  
Focal Point ◽  
Optical Emission ◽  
Target Surface ◽  
Laser Induced Breakdown Spectroscopy ◽  
Spatial Confinement ◽  
Laser Induced Plasma ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

The spatial confinement effect in laser-induced plasma with different distances between the target surface and focal point is investigated by optical emission spectroscopy.

scholarly journals Application of Scikit and Keras Libraries for the Classification of Iron Ore Data Acquired by Laser-Induced Breakdown Spectroscopy (LIBS)

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1393 ◽  
Author(s):  
Yanwei Yang ◽  
Xiaojian Hao ◽  
Lili Zhang ◽  
Long Ren
Keyword(s):  
Machine Learning ◽  
Iron Ore ◽  
Laser Induced Breakdown Spectroscopy ◽  
Support Vector ◽  
Iron Ores ◽  
K Nearest Neighbor ◽  
Machine Model ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Due to the complexity of, and low accuracy in, iron ore classification, a method of Laser-Induced Breakdown Spectroscopy (LIBS) combined with machine learning is proposed. In the research, we collected LIBS spectra of 10 iron ore samples. At the beginning, principal component analysis algorithm was employed to reduce the dimensionality of spectral data, then we applied k-nearest neighbor model, neural network model, and support vector machine model to the classification. The results showed that the accuracy of three models were 82.96%, 93.33%, and 94.07% respectively. The results also demonstrated that LIBS with machine learning model exhibits an excellent classification performance. Therefore, LIBS technique combined with machine learning can achieve a rapid, precise classification of iron ores, and can provide a completely new method for iron ores’ selection in the metallurgical industry.

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