Volume 66 Issue 5
Jul.  2022
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LI Ke, LU Bing, DU Biao, LU Xiaoxin, LIU Zhe, LI Qingwu, ZHANG Zhengdong. Effective Selection and Application of Ethanol Characteristic Spectrum in Gasoline[J]. Metrology Science and Technology, 2022, 66(5): 19-24. doi: 10.12338/j.issn.2096-9015.2021.0631
Citation: LI Ke, LU Bing, DU Biao, LU Xiaoxin, LIU Zhe, LI Qingwu, ZHANG Zhengdong. Effective Selection and Application of Ethanol Characteristic Spectrum in Gasoline[J]. Metrology Science and Technology, 2022, 66(5): 19-24. doi: 10.12338/j.issn.2096-9015.2021.0631

Effective Selection and Application of Ethanol Characteristic Spectrum in Gasoline

doi: 10.12338/j.issn.2096-9015.2021.0631
  • Available Online: 2022-04-14
  • Publish Date: 2022-07-11
  • Near-infrared spectroscopy rapid analysis is one of the main methods to detect the ethanol content in gasoline, and the selection of the spectrum is an important factor affecting the prediction accuracy of the rapid detection model. In this study, a rapid analysis method of near-infrared spectroscopy based on an effective characteristic spectrum was established to improve the accuracy of detection of ethanol content in gasoline. By comparing the near-infrared spectra of gasoline with different concentrations of ethanol content, the effective characteristic spectrum of ethanol molecules in gasoline was determined to be 4524.183~5044.869 cm−1 and 5985.961~7108.329 cm−1. The optimal pre-processing method for near-infrared spectroscopy was chosen, and modeling analysis was carried out using the full-spectrum and effective characteristic spectrum. The relevant parameters of the model established using the effective characteristic spectrum are as follows: the root-mean-square error of cross-validation (RMSECV) is 0.5849, the internal cross-validation correlation coefficient (${{R}}_{\text{CV}}^{\text{2}}$) is 0.9991, the root-mean-square error of prediction (RMSEP) is 0.6090, and the external verification correlation coefficient (${{R}}_{\text{P}}^{\text{2}}$) of the prediction set is 0.9989. Compared with the full-spectrum modeling analysis, the RMSECV was reduced by 30.27 %, and the RMSEP was reduced by 18.58%. In conclusion, the quantitative analysis model established by using the characteristic spectrum has higher accuracy and can meet the need for rapid analysis of ethanol content in gasoline.
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