Citation: WEN Guo-Ji, DAI Lian-Kui, LIU Wei. Raman Spectroscopic Quantitative Analysis Based on Genetic Algorithm and Linear Superposition Principle[J]. Chinese Journal of Analytical Chemistry, ;2021, 49(1): 85-94. doi: 10.19756/j.issn.0253-3820.201500 shu

Raman Spectroscopic Quantitative Analysis Based on Genetic Algorithm and Linear Superposition Principle

College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China

Corresponding author: DAI Lian-Kui, lkdai@zju.edu.cn
Received Date: 16 August 2020
Revised Date: 4 November 2020

Fund Project: Supported by the National Natural Science Foundation of China (No.U1609213).

A Raman quantitative analysis algorithm based on genetic algorithm (GA) and linear additive model was developed. When the Raman spectrum of one main substance in a batch of mixed samples was known, the characteristic peak position of each component could be obtained by correlation analysis. Then genetic algorithm (GA) was used to fit and optimize the corresponding spectral matrix of the pure substance. Finally, the obtained spectral matrix was used to establish the Raman quantitative analysis model of various substances in the mixture. This method only required a large amount of calculation when optimizing model parameters in the model training process, and only needed to perform simple matrix operations in actual prediction, showing a small real-time calculation amount. The developed method was suitable for continuous online applications, and could be applied to the quantitative analysis of liquid and gas mixture. By taking natural gas as an example, when the Raman spectrum of the main component in methane was known, based on a set of natural gas training samples whose composition and Raman spectrum were known, a quantitative analysis model based on Raman spectrum for main components in natural gas (CH₄, C₂H₆, C₃H₈, CO₂, N₂, H₂, CO) was developed. For the test sample set, the corresponding root mean square error (RMSE) were 0.488%, 0.103%, 0.358%, 0.079%, 0.087%, 0.126% and 0.022%, and the multiple correlation coefficient (R²) were 0.995, 0.991, 0.990, 0.991, 0.998, 0.999 and 0.999, respectively. Although the result was just slightly inferior to the result when the spectra of all pure substances were known, the algorithm presented here provided a solution for the case when some pure substance spectra were unknown.
- Genetic algorithm,
- Raman spectroscopy,
- Quantitative analysis,
- Natural gas,
- Linear additive
1. [1]
2. [2]
3. [3]
4. [4]
5. [5]
6. [6]
7. [7]
8. [8]
9. [9]
10. [10]
11. [11]
12. [12]
13. [13]
14. [14]
15. [15]
16. [16]
17. [17]
18. [18]
19. [19]
20. [20]
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