Laser-Induced Breakdown Spectroscopy-Based Coal-Rock Recognition: An <i>in Situ</i> Sampling and Recognition Method

Liu, Cong; Luo, Yi; Zhang, Qizhi

doi:10.1109/access.2021.3133886

Cited by 6 publications

(4 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The spectral emission lines of the CN molecular band were identified at 388.3, 387.1, 386.2, 385.5, and 385.1 nm that were associated with the vibrational transitions (0,0), (1,1), (2,2), (3,3), and (4,4), respectively. The spectral emission lines of the C 2 Swan band were identified at 467.3, 468.3, 469.8, 471.6, 473.7, 509.6, 512.9, 516.5, 550.2, 554.1, 558.5, 563.5, 600.5, 606.0, and 612.0 nm that were associated with the vibrational transitions (5,4), (4,3), (3,2), (2,1), (1,0), (2,2), (1,1), (0,0), (3,4), (2,3), (1,2), (0,1), (3,5), (2,4), and (1,3), respectively. The transitions among two electronic levels were established using the variance in vibrational quantum statistics from the higher to lower electronic state.…”

Section: Libs Spectra Analysismentioning

confidence: 99%

“…Laser-induced breakdown spectroscopy (LIBS) is a highly versatile analytical technique that is capable of performing qualitative and quantitative chemical analysis on a wide range of materials. It has been successfully applied in numerous fields, including but not limited to, plastics [1], alloys [2], rocks [3,4], aerosols [5], cement powders [6], radioactive substances [7], fissionable materials [8], explosives [9], polymers [10,11], mineral exploration [12], mining [13][14][15][16][17], and waste industrial substances [18][19][20][21]. LIBS offers the ability to characterize and measure the elemental composition and concentration of these materials, thereby making it an invaluable tool for various research, industrial, and environmental applications.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Spectroscopic Characterization of MgO/Nylon (6/6) Polymer: Evaluating the Potential of LIBS and Statistical Methods

et al. 2023

View full text Add to dashboard Cite

The potential of using laser-induced breakdown spectroscopy (LIBS) in combination with various other spectroscopic and statistical methods was assessed for characterizing pure and MgO-doped nylon (6/6) organic polymer samples. The pure samples, obtained through a polycondensation chemical technique, were artificially doped with MgO prior to analysis for comparative purposes. These artificially doped samples served as crucial reference materials for comparative analysis and reference purposes. The LIBS studies were performed under local thermodynamic equilibrium (LTE) and optically thin plasma conditions. To assess the structural crystallinity of the nylon (6/6) polymer samples, X-ray diffraction (XRD) analysis, and Fourier transform infrared (FTIR) spectroscopy were employed to detect functional groups such as N-H, C-H, and C-N in the adsorbent polyamide nylon sample. Additionally, diffuse reflectance spectroscopy (DRS) analysis was conducted to investigate the effects of doping and temperature on the band gap and material reflectance across different sample temperatures. Chemical compositional analysis was performed using X-ray photoelectron spectroscopy (XPS) with the carbon C1s peak at 248.8 eV serving as a reference for spectrum calibration, along with energy-dispersive X-ray (EDX) analysis, which demonstrated good agreement between the techniques. To validate the different methodologies, the results obtained from CF-LIBS and EDX were compared with those from the standard inductively coupled plasma mass spectrometry (ICP-MS) technique. Finally, for classification analysis, principal component analysis (PCA) was applied to the LIBS spectral data at different sample temperatures (25 °C, 125 °C, 225 °C, and 325 °C). The analyses demonstrated that the combination of LIBS with PCA, along with other methods, presents a robust technique for polymer characterization.

show abstract

Section: Libs Spectra Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Spectroscopic Characterization of MgO/Nylon (6/6) Polymer: Evaluating the Potential of LIBS and Statistical Methods

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Laser-induced breakdown spectroscopy (LIBS) is an atomic emission spectroscopy technique with the advantages of in situ, real-time, rapid, and simultaneous multi-element detection [ 7 , 8 ]. The LIBS technique is applied in various fields, such as biological tissue detection [ 9 , 10 , 11 ], explosives detection [ 12 , 13 , 14 ], coal analysis [ 15 , 16 , 17 ], polymer identification analysis [ 18 , 19 , 20 ], food analysis [ 21 , 22 , 23 ], alloy analysis [ 24 , 25 , 26 ], ore identification analysis [ 27 , 28 , 29 ], and soil element detection [ 30 , 31 , 32 ].…”

Section: Introductionmentioning

confidence: 99%

Rapid Test for Adulteration of Fritillaria Thunbergii in Fritillaria Cirrhosa by Laser-Induced Breakdown Spectroscopy

Wei

Teng

Wang

et al. 2023

Foods

View full text Add to dashboard Cite

Fritillaria has a long history in China, and it can be consumed as medicine and food. Owing to the high cost of Fritillaria cirrhosa, traders sometimes mix it with the cheaper Fritillaria thunbergii powder to make profit. Herein, we proposed a laser-induced breakdown spectroscopy (LIBS) technique to test the adulteration present in the sample of Fritillaria cirrhosa powder. Experimental samples with different adulteration levels were prepared, and their LIBS spectra were obtained. Partial least squares regression (PLSR) was adopted as the quantitative analysis model to compare the effects of four data standardization methods, namely, mean centring, normalization by total area, standard normal variable, and normalization by the maximum, on the performance of the PLSR model. Principal component analysis and least absolute shrinkage and selection operator (LASSO) were utilized for feature extraction and feature selection, and the performance of the PLSR model was determined based on its quantitative analysis. Subsequently, the optimal number of features was determined. The residuals were corrected using support vector regression (SVR). The mean absolute error and root mean square error of prediction obtained from the quantitative analysis results of the combined LASSO-PLSR-SVR model for the test set data were 5.0396% and 7.2491%, respectively, and the coefficient of determination R2 was 0.9983. The results showed that the LIBS technique can be adopted to test adulteration in the sample of Fritillaria cirrhosa powder and has potential applications in drug quality control.

show abstract

“…Cao et al used a combination of LIBS and KNN to classify coal types based on 11 elements in coal. Liu et al proposed a method for classifying coal and rock, which constructs a simplified spectral model (SSM) of LIBS and realizes the accurate recognition of coal and rock in unmanned coal mining scenarios based on SSM and neural networks. Ma et al utilized a stepwise classification method to separate coal from common detritus and improve the accuracy of coal analysis.…”

Section: Introductionmentioning

confidence: 99%

Coal and Gangue Classification Based on Laser-Induced Breakdown Spectroscopy and Deep Learning

Xu,

Mao,

Yan

et al. 2023

ACS Omega

View full text Add to dashboard Cite

During the extraction and processing of coal, a large amount of solid waste, collectively known as gangue, is produced. This gangue has a low carbon content but a high ash content, accounting for approximately 15 to 20% of the total coal yield. Before coal is used, coal and gangue must be effectively separated to reduce the gangue content in the raw coal and improve the efficiency of coal utilization. This study introduces a classification method for coal and gangue based on a combination of laser-induced breakdown spectroscopy (LIBS) and deep learning. The method employs Gramian angular summation fields (GASF) to convert 1D spectral data into 2D time-series data, visualizing them as 2D images, before employing a novel deep learning model—GASF-CNN—for coal and gangue classification. GASF-CNN enhances model focus on critical features by introducing the SimAM attention mechanism, and additionally, the fusion of various levels of spectral features is achieved through the introduction of residual connectivity. GASF-CNN was trained and tested using a spectral data set containing coal and gangue. Comparative experimental results demonstrate that GASF-CNN outperforms other machine learning and deep learning models across four evaluation metrics. Specifically, it achieves 98.33, 97.06, 100, and 98.51% in the accuracy, recall, precision, and F1 score metrics, respectively, thereby achieving an accurate classification of coal and gangue.

show abstract

Laser-Induced Breakdown Spectroscopy-Based Coal-Rock Recognition: An in Situ Sampling and Recognition Method

Cited by 6 publications

References 50 publications

Multi-Spectroscopic Characterization of MgO/Nylon (6/6) Polymer: Evaluating the Potential of LIBS and Statistical Methods

Multi-Spectroscopic Characterization of MgO/Nylon (6/6) Polymer: Evaluating the Potential of LIBS and Statistical Methods

Rapid Test for Adulteration of Fritillaria Thunbergii in Fritillaria Cirrhosa by Laser-Induced Breakdown Spectroscopy

Coal and Gangue Classification Based on Laser-Induced Breakdown Spectroscopy and Deep Learning

Contact Info

Product

Resources

About