Ai-ling TAN, Zhen-yuan CHU, Xiao-si WANG, Yong ZHAO. Detection of Pearl Powder Adulteration Based on Raman Spectroscopy and DCGAN Data Enhancement[J]. Spectroscopy and Spectral Analysis, 2022, 42(3): 769
- Spectroscopy and Spectral Analysis
- Vol. 42, Issue 3, 769 (2022)
Fig. 1. Mean Raman spectra of the samples (a): Original spectra; (b): Spectra with pretreatment
Fig. 2. Generative adversarial network flow chart
Fig. 3. DCGAN structure diagram
Fig. 4. Original spectrum and generated spectrum based on DCGAN
(a): Original spectra; (b): Generated spectra
(a): Original spectra; (b): Generated spectra
Fig. 5. Correlation curve between real and predicted purity of quantitative models built by different data enhancement methods combined with 1DCNN
(a): DCGAN-1DCNN; (b): Noise addition-1DCNN; (c): Translation-1DCNN; (d): Noise+Translation-1DCNN
(a): DCGAN-1DCNN; (b): Noise addition-1DCNN; (c): Translation-1DCNN; (d): Noise+Translation-1DCNN
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Table 1. Parameters of generate network
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Table 2. Parameters of discriminating network
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Table 3. Similarity evaluation between the spectra generated by traditional data enhancement and DCGAN enhancement methods and the original spectra
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Table 4. Comparison of the identification results of adulterated pearl powder
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Table 5. Comparison of quantitative models built by different data enhancement methods combined with 1DCNN
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