Haochen Li, Tianyuan Liu, Yuchao Fu, Wanxiang Li, Meng Zhang, Xi Yang, Di Song, Jiaqi Wang, You Wang, Meizhen Huang. Rapid classification of copper concentrate by portable laser-induced breakdown spectroscopy combined with transfer learning and deep convolutional neural network[J]. Chinese Optics Letters, 2023, 21(4): 043001
- Chinese Optics Letters
- Vol. 21, Issue 4, 043001 (2023)
Fig. 1. Block diagram of portable LIBS setup.
Fig. 2. Typical spectrum of copper concentrate acquired by portable LIBS apparatus.
Fig. 3. Elemental spectral line intensities of the raw spectra of copper concentrates from 11 classes.
Fig. 4. PCA plot of the raw spectra of copper concentrates from 11 classes.
Fig. 5. Steps for conversion of 1D spectra to 2D matrix.
Fig. 6. Schematic diagram of 2D spectrum (left) and 2D spectrum image (right).
Fig. 7. Training process of the four CNN models.
Fig. 8. Confusion matrices of the four CNN models on the test set (two upper panels, VGG16 and ResNet18; two lower panels, DenseNet121 and InceptionV3).
Fig. 9. Comparison of classification accuracy between CNN models and traditional machine-learning models on the test set.
Fig. 10. Schematic diagram of the spectral features selected by the CST methods.
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Table 1. Copper Contents of the Copper Concentrate Samples
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Table 2. Performance of the CNN Models with the Convolutional Layers Frozen and Only Fully Connected Layers Trained
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Table 3. Performance of the CNN Model with the Convolutional Layer Unfrozen and Trained with All Parameters
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Table 4. Performance of the Four Machine-Learning Models
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