Citation: LI Mao-Gang, YAN Chun-Hua, XUE Jia, ZHANG Tian-Long, LI Hua. Rapid Quantitative Analysis of Methanol Content in Methanol Gasoline by Near Infrared Spectroscopy Coupled with Wavelet Transform-Random Forest[J]. Chinese Journal of Analytical Chemistry, ;2019, 47(12): 1995-2003. doi: 10.19756/j.issn.0253-3820.191372 shu

Rapid Quantitative Analysis of Methanol Content in Methanol Gasoline by Near Infrared Spectroscopy Coupled with Wavelet Transform-Random Forest

LI Mao-Gang ^1, ,
YAN Chun-Hua ² ,
XUE Jia ¹ ,
ZHANG Tian-Long ² ,
LI Hua ^1,2

1.
College of Chemistry and Chemical Engineering, Xi'an Shiyou University, Xi'an 710065, China;
2.
Key Laboratory of Synthetic and Natural Functional Molecular Chemistry of Ministry of Education, College of Chemistry and Material Science, Northwest University, Xi'an 710127, China

Received Date: 2 July 2019
Revised Date: 29 September 2019

Fund Project: This work was supported by the National Natural Science Foundation of China (Nos. 21873076, 21675123, 21605123), the Natural Science Basic Research Plan in Shaanxi Province of China (No. 2018JQ2013), the Scientific Research Plan Projects of Shaanxi Education Department (No. 17JK0780), the Graduate Innovation and Practice Ability Development Project of Xi'an Shiyou University (No. YCS19121013) and the Northwest University Graduate Innovation and Creativity Funds (No. YZZ17126).

A rapid quantitative analysis method for detection of methanol content in methanol gasoline based on near infrared spectroscopy (NIR) coupled with wavelet transform-random forest algorithm(WT-RF) was developed. Firstly, spectra of 54 methanol gasoline samples were collected by Fourier transform infrared spectroscopy, and spectral analysis was also carried out. Secondly, the effects of different spectral pretreatment methods were explored, and an optimized spectral pretreatment method based on WT was obtained by using different wavelet basis functions and wavelet decomposition levels. Thirdly, an optimized input variable of RF calibration model was obtained by selecting different variable importance threshold values. Finally, an optimized WT-RF calibration model was constructed by using the optimized modeling parameters of wavelet basis function (db5), decomposition layers (4) and variable importance threshold value (0.0002). To further explore the predictive performance of WT-RF calibration model, the predictive results were compared with that of wavelet transform-partial least squares (WT-PLS) calibration models and wavelet transform-least square support vector machine (WT-LSSVM) calibration models. The WT-RF calibration model showed the best predictive performance, and its coefficient of determination of cross-validation (R_cv²) and root mean square error of cross-validation (RMSECV) were 0.9990 and 0.0044%, respectively. Coefficient of determination of prediction set (R_p²) and root mean square error of prediction set (RMSEP) were 0.9885 and 0.0191%, respectively. Therefore, NIR coupled with WT-RF algorithm was an effective method for the detection of methanol content in methanol gasoline.
- Near infrared spectroscopy,
- Methanol gasoline,
- Wavelet transform,
- Random forest
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