Accurate identification of methanol and ethanol gasoline types and rapid detection of the alcohol content using effective chemical information

Abstract

Methanol and ethanol gasoline are two emerging clean energy sources with different characteristics. To achieve the qualitative identification and quantitative analysis of the alcohols present in methanol and ethanol gasoline, effective chemical information (ECI) models based on the characteristic spectral bands of the near-infrared (NIR) spectra of the methanol and ethanol molecules were developed using the partial least squares discriminant analysis (PLS-DA) and partial least squares (PLS) algorithms. The ECI model was further compared with models built from the full wavenumber (Full) spectra, variable importance in projection (VIP) spectra, and Monte Carlo uninformative variable elimination (MC-UVE) spectra to determine the predictive performance of ECI model. Among the various qualitative identification models, it was found that the ECI-PLS-DA model, which is built using the differences in molecular chemical information between methanol and ethanol, exhibited sensitivity, specificity and accuracy values of 100%. The ECI-PLS-DA model accurately identified methanol gasoline and ethanol gasoline with different contents. In the quantitative analysis model for methanol gasoline, the methanol gasoline and ethanol gasoline ECI-PLS models exhibited the smallest root mean squared error of predictions (RMSEP) of 0.18 and 0.21% (v/v), respectively, compared to the other models. Meanwhile, the F-test and T-test results revealed that the NIR method employing the ECI-PLS model showed no significant difference compared to the standard method. Compared with other spectral models examined herein, the ECI model demonstrated the highest recognition success and determination accuracy. This study therefore established a highly accurate and rapid determination model for the qualitative identification and quantitative analysis based on chemical structures. It is expected that this model could be extended to the NIR analysis of other physicochemical properties of fuel.