Raman spectroscopy for the discrimination and quantification of fuel blends

Biodiesel is an alternative energy source to replace fossil fuels and reduce the environmental pollution. Adding biodiesel in fossil diesel can increase the oxygen content (from fatty acid) and promote fuel to be burned more quickly and thoroughly. However, the biodiesel content criterion of different countries was diverse from each other. In this study, Raman spectroscopy was used as a tool in classifying fuel blends and quantifying biodiesel contents. For classifying the fuel blends, principal component analysis (PCA) method was employed, where 87.22% of spectral variation was characterized by the first two components PCA scores shows a clear discrimination between the pure fuels and mixture fuels. Meanwhile, for identifying and quantifying the blends of diesel and biodiesel, Raman spectroscopy analysis based on partial least squares (PLS) regression was conducted. Biodiesel mainly present three characteristic Raman regions corresponding to the spectroscopy of diesel. The CH Raman region presents the better quantitative capacity than the CC and CO spectral regions. And the PLS regression built from CH Raman spectral region in quantifying biodiesel contents presents a higher correlation coefficient and lower root mean square error for prediction. Furthermore, employing only CH Raman region coupled with PLS regression for predicting concentration of biodiesel can reduce an order of magnitude of root mean square error compared with using three characteristic Raman spectral regions together. Our result show that Raman spectroscopy combined with PCA and PLS can identify fuels and biofuels for discrimination and quantitation.