No abstract

Brazilian automotive gasoline has been the target of constant adulterations in an attempt to raise profit margins as a result of the market flexibility and the ensuing increased competition. Gasoline adulteration with solvents is common because solvents present lower taxation in comparison to gasoline. In the face of this, the importance of developing low-cost analytical techniques such as spectroscopy to certify the quality and authenticity of fuels used in Brazil is conspicuous. This work used IR spectroscopy and multivariate techniques (principal components analysis-linear discriminant analysis, PCA-LDA) to determine gasoline adulteration by solvent and to identify the solvent added. The results show that FTIR associated with PCA-LDA is a powerful technique in the quality control of automotive gasoline. The method sensitivity was 8% v/v with 96% efficiency in the classification of adulterated and unadulterated gasoline and 93% efficiency in the identification of the type of solvent added.

The partial least-squares (PLS) calibration method as a chemometric tool was used to develop a calibration model using Fourier transform infrared spectroscopy (FTIR) spectra data of biodiesel samples from different sources, such as cotton, castor, and palm, which were mixed with raw soybean oil to simulate an adulteration system. The PLS calibration method was applied with and without variable selection to quantify the amount of raw soybean oil present in these samples. Classic methods of variable selection, such as forward and stepwise, were applied to all origins together and each one separately. Variable selection improves not only the stability of the model to the colinearity in multivariate spectra but also the interpretability of the relationship between the model and the sample composition, which means that it becomes easier to determine and quantify the amount of raw soybean oil mixed in each biodiesel source.