Fusion of Laser-Induced Breakdown Spectroscopy and Raman Spectroscopy for Mineral Identification Based on Machine Learning
Abstract
1. Introduction
2. Results and Discussion
2.1. LIBS and RS Spectra
2.2. Mineral Classification
2.3. Mineral Identification
3. Experiment Methods
3.1. Mineral Samples
3.2. Integrated LIBS-RS System
3.3. Spectral Data Processing
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Data Sets | Raw Data | Pre-Processed Data | |
---|---|---|---|
LIBS | Training | 92.63% ± 3.78% ±3.78 | 94.10% ± 3.66% ±3.66 |
Test | 94.42% ± 4.70% ±4.70 | 95.61% ± 2.45% ±2.45 | |
RS | Training | 90.36% ± 4.39% ±4.39 | 93.34% ± 3.49% ±3.49 |
Test | 94.74% ± 5.47% ±5.47 | 95.61% ± 4.33% ±4.33 |
Method | LIBS | Raman | Fusion | |
---|---|---|---|---|
Training | PLS-DA | 94.10 ±3.66 | 93.34 ± 3.49 | 96.12 ± 3.49 |
K-ELM | 97.12 ± 3.37 | 96.98 ± 3.19 | 97.67 ± 3.25 | |
Test | PLS-DA | 95.61 ± 2.45 | 95.61 ± 4.33 | 97.24 ± 1.44 |
K-ELM | 97.78 ± 1.53 | 97.73 ± 1.48 | 98.39 ± 1.27 |
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Dai, Y.; Liu, Z.; Zhao, S. Fusion of Laser-Induced Breakdown Spectroscopy and Raman Spectroscopy for Mineral Identification Based on Machine Learning. Molecules 2024, 29, 3317. https://doi.org/10.3390/molecules29143317
Dai Y, Liu Z, Zhao S. Fusion of Laser-Induced Breakdown Spectroscopy and Raman Spectroscopy for Mineral Identification Based on Machine Learning. Molecules. 2024; 29(14):3317. https://doi.org/10.3390/molecules29143317
Chicago/Turabian StyleDai, Yujia, Ziyuan Liu, and Shangyong Zhao. 2024. "Fusion of Laser-Induced Breakdown Spectroscopy and Raman Spectroscopy for Mineral Identification Based on Machine Learning" Molecules 29, no. 14: 3317. https://doi.org/10.3390/molecules29143317
APA StyleDai, Y., Liu, Z., & Zhao, S. (2024). Fusion of Laser-Induced Breakdown Spectroscopy and Raman Spectroscopy for Mineral Identification Based on Machine Learning. Molecules, 29(14), 3317. https://doi.org/10.3390/molecules29143317