scholarly journals Rapid Elemental Determination And Discrimination Of Coins Using Laser-Induced Breakdown Spectroscopy

2021 ◽  
Vol 42 (4) ◽  
Author(s):  
Yuzhu Liu
Keyword(s):  
Principal Component ◽  
Nearest Neighbors ◽  
Original Data ◽  
Laser Induced Breakdown Spectroscopy ◽  
K Nearest Neighbors ◽  
Breakdown Spectroscopy ◽  
Characteristic Lines ◽  
Elemental Determination ◽  
Laser Induced Breakdown ◽  
Good Agreement

Damaged coins can be identified effectively via spectral analysis based on LIBS, which is of great significance for coin recycling. This paper takes the Renminbi (RMB), the Chinese currency, as the example, including the denominations of YI FEN, ER FEN, WU FEN, YI JIAO, WU JIAO and YI YUAN. Some characteristic lines of Mg, Al, Fe, Cr, Cu, Sn, Ni, Na and Ca were observed in the spectra, as well as the molecular bands of AlO. Principal component analysis (PCA) was used to reduce the dimension of the spectra of the different RMB coins. The samples after dimension reduction are classified by k-Nearest Neighbors (KNN), and 4 categories were obtained with a classification accuracy of 100%. Further, new spectra of different denomination RMB coins were added to the original data for the same analysis. The results are in good agreement which shows the potential of the combination of LIBS, PCA and KNN for the analysis and identification of different coins.

A Comparative Study of Two Data Reduction Methods for Steel Classification Based on LIBS

2014 ◽  
Vol 644-650 ◽  
pp. 4722-4725 ◽  
Author(s):  
Hai Yang Kong ◽  
Lan Xiang Sun ◽  
Jing Tao Hu ◽  
Yong Xin ◽  
Zhi Bo Cong
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Principal Component ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Characteristic Lines ◽  
Laser Induced Breakdown ◽  
Reduction Methods ◽  
Artificial Neural ◽  
Priori Knowledge

Spectra of 27 steel samples were acquired by Laser-Induced Breakdown Spectroscopy (LIBS) for steel classification. Two methods were used to reduce dimensions: the first is to select characteristic lines of elements contained in the samples manually and the second is to do principal component analysis (PCA) of original spectra. Then the data after reducing dimensions was used as the input of artificial neural networks (ANN) to classify steel samples. The results show that, the better result can be achieved by selecting peak lines manually, but this solution needs much priori knowledge and wastes much time. The principal components (PCs) of original spectra were utilized as the input of artificial neural networks can also attain a good result nevertheless and this method can be developed into an automatic solution without any priori knowledge.

A NOVEL STRATEGY FOR DIRECT ELEMENTAL DETERMINATION USING LASER-INDUCED BREAKDOWN SPECTROSCOPY: FLUENCE CALIBRATION

2021 ◽  
Author(s):  
Raquel C Machado ◽  
Diego Victor Babos ◽  
Daniel Fernandes Andrade ◽  
Edenir Rodrigues Pereira-Filho
Keyword(s):  
Quantitative Analysis ◽  
Matrix Effects ◽  
Calibration Method ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Elemental Determination ◽  
Laser Induced Breakdown ◽  
Direct Solid Analysis ◽  
Novel Strategy ◽  
Direct Solid

Quantitative analysis requires several efforts to obtain an adequate calibration method to overcome matrix effects employing direct solid analysis by laser-induced breakdown spectroscopy (LIBS). To this end, in this study,...

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.

Classification and identification of brands of iron ores using laser-induced breakdown spectroscopy combined with principal component analysis and artificial neural networks

2020 ◽  
Vol 12 (10) ◽  
pp. 1316-1323 ◽  
Author(s):  
Yawen Yang ◽  
Chen Li ◽  
Shu Liu ◽  
Hong Min ◽  
Chenglin Yan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Principal Component Analysis ◽  
Artificial Neural Networks ◽  
Principal Component ◽  
Component Analysis ◽  
Laser Induced Breakdown Spectroscopy ◽  
Iron Ores ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Ann Models

In this work, PCA-ANN models of LIBS spectra were developed to classify and identify iron ores according to the production countries and brands.

scholarly journals Laser-Induced Breakdown Spectroscopy and Principal Component Analysis for the Classification of Spectra from Gold-Bearing Ores

2019 ◽  
Vol 74 (1) ◽  
pp. 42-54 ◽  
Author(s):  
Daniel Diaz ◽  
Alejandro Molina ◽  
David W. Hahn
Keyword(s):  
Principal Component Analysis ◽  
Limit Of Detection ◽  
Principal Component ◽  
Component Analysis ◽  
Emission Lines ◽  
Laser Induced Breakdown Spectroscopy ◽  
Single Shot ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Laser-induced breakdown spectroscopy (LIBS) and principal component analysis (PCA) were applied to the classification of LIBS spectra from gold ores prepared as pressed pellets from pulverized bulk samples. For each sample, 5000 single-shot LIBS spectra were obtained. Although the gold concentrations in the samples were as high as 7.7 µg/g, Au emission lines were not observed in most single-shot LIBS spectra, rendering the application of the usual ensemble-averaging approach for spectral processing to be infeasible. Instead, a PCA approach was utilized to analyze the collection of single-shot LIBS spectra. Two spectral ranges of 21 nm and 0.15 nm wide were considered, and LIBS variables (i.e., wavelengths) reduced to no more than three principal components. Single-shot spectra containing Au emission lines (positive spectra) were discriminated by PCA from those without the spectral feature (negative spectra) in a spectral range of less than 1 nm wide around the Au(I) 267.59 nm emission line. Assuming a discrete gold distribution at very low concentration, LIBS sampling of gold particles seemed unlikely; therefore, positive spectra were considered as data outliers. Detection of data outliers was possible using two PCA statistical parameters, i.e., sample residual and Mahalanobis distance. Results from such a classification were compared with a standard database created with positive spectra identified with a filtering algorithm that rejected spectra with an Au intensity below the smallest detectable analytical LIBS signal (i.e., below the LIBS limit of detection). The PCA approach successfully identified 100% of the data outliers when compared with the standard database. False identifications in the multivariate approach were attributed to variations in shot-to-shot intensity and the presence of interfering emission lines.

scholarly journals A Quick Classifying Method for Tracking and Erosion Resistance of HTV Silicone Rubber Material via Laser-Induced Breakdown Spectroscopy

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1087 ◽  
Author(s):  
Ping Chen ◽  
Xilin Wang ◽  
Xun Li ◽  
Qishen Lyu ◽  
Naixiao Wang ◽  
...  
Keyword(s):  
Silicone Rubber ◽  
Erosion Resistance ◽  
Performance Enhancement ◽  
Filler Content ◽  
Principal Component ◽  
Laser Induced Breakdown Spectroscopy ◽  
Rubber Material ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Resistance Performance

Silicone rubber material is widely used in high-voltage external insulation systems due to its excellent hydrophobicity and hydrophobicity transfer performance. However, silicone rubber is a polymeric material with a poor ability to resist electrical tracking and erosion; therefore, some fillers must be added to the material for performance enhancement. The inclined plane test is a standard method used for evaluating the tracking and erosion resistance by subjecting the materials to a combination of voltage stress and contaminate droplets to produce failure. This test is time-consuming and difficult to apply in field inspection. In this paper, a new and faster way to evaluate the tracking and erosion resistance performance is proposed using laser-induced breakdown spectroscopy (LIBS). The influence of filler content on the tracking and erosion resistance performance was studied, and the filler content was characterized by thermogravimetric analysis and the LIBS technique. In this paper, the tracking and erosion resistance of silicone rubber samples was correctly classified using principal component analysis (PCA) and neural network algorithms based on LIBS spectra. The conclusions of this work are of great significance to the performance characterization of silicone rubber composite materials.

Sign in / Sign up

Export Citation Format

Share Document