AI Article Synopsis
- The estimation of physicochemical parameters like distillation points and relative densities is crucial for gasoline quality control, but traditional methods are time-consuming and require specific equipment.
- An alternative approach using multivariate analysis of comprehensive two-dimensional gas chromatography with flame ionization detection (GC×GC-FID) data was developed, utilizing gasoline samples tested by standard procedures to create regression models.
- The results showed promising prediction accuracy, with root mean square prediction differences (RMSPD) as low as 0.24% for relative density and up to 1.71% for distillation points, suggesting that this method could effectively predict key physicochemical properties of gasoline.
Article Abstract
The estimation of physicochemical parameters such as distillation points and relative densities still plays an important role in the quality control of gasoline and similar fuels. Their measurements according to standard ASTM procedures demands specific equipments and are time and work consuming. An alternative method to predict distillation points and relativity density by multivariate analysis of comprehensive two-dimensional gas chromatography with flame ionization detection (GC×GC-FID) data is presented here. Gasoline samples, previously tested according to standard methods, were used to build regression models, which were evaluated by external validation. The models for distillation points were built using variable selection methods, while the model for relativity density was built using the whole chromatograms. The root mean square prediction differences (RMSPD) obtained were 0.85%, 0.48%, 1.07% and 1.71% for 10, 50 and 90% v/v of distillation and for the final point of distillation, respectively. For relative density, the RMSPD was 0.24%. These results suggest that GC×GC-FID combined with multivariate analysis can be used to predict these physicochemical properties of gasoline.
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| http://dx.doi.org/10.1016/j.chroma.2011.01.056 | DOI Listing |
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