Chinese Journal of Lasers, Volume. 46, Issue 3, 0311005(2019)
Surface-Enhanced Raman Spectroscopy Quantitative Analysis of Polycyclic Aromatic Hydrocarbons Based on Support Vector Machine Algorithm
Fig. 1. SERS and Raman spectra of pyrene, phenanthrene and their mixed solution. (a) Blank substrate; (b) SERS spectrum of pyrene and phenanthrene mixed solution; (c) SERS spectrum of phenanthrene solution; (d) Raman spectrum of solid phenanthrene; (e) SERS spectrum of pyrene solution; (f) Raman spectrum of solid pyrene
Fig. 2. SERS and Raman spectra of mixed solution of internal standard and measured samples. (a) Raman spectrum of solid KSCN; (b) SERS spectrum of mixed solution of internal standard and pyrene; (c) SERS spectrum of mixed solution of internal standard and phenanthrene
Fig. 3. SERS spectra of different concentrations of pyrene after addition of internal standard. (a) Blank substrate; (b) 1×10-9 mol·L-1; (c) 1×10-8 mol·L-1; (d) 1×10-7 mol·L-1
Fig. 4. SERS spectra of different concentrations of phenanthrene after addition of internal standard. (a) Blank substrate; (b) 1×10-9 mol·L-1; (c) 1×10-8 mol·L-1; (d) 1×10-7 mol·L-1
Fig. 5. Comparison between predicted and true values of pyrene based on three methods. (a) GS-SVR; (b) GA-SVR; (c) PSO-SVR
Fig. 6. Predicted and true values of mixed solution of pyrene and phenanthrene based on GS-SVR. (a) Pyrene; (b) phenanthrene
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Yang Chen, Xia Yan, Xu Zhang, Xiaofeng Shi, Jun Ma. Surface-Enhanced Raman Spectroscopy Quantitative Analysis of Polycyclic Aromatic Hydrocarbons Based on Support Vector Machine Algorithm[J]. Chinese Journal of Lasers, 2019, 46(3): 0311005
Category: spectroscopy
Received: Oct. 22, 2018
Accepted: Dec. 18, 2018
Published Online: May. 9, 2019
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