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In this work, a multivariate approach was used to classify diesel/biodiesel fuel blends among 0% to 100% of biodiesel content on fuel mixture through discriminant analysis and cluster analysis associated with Fourier transform infrared spectroscopy (FTIR). The multivariate statistical techniques used in this work were partial least squares discriminant analysis (PLS-DA), principal component analysis (PCA), soft independent modeling of class analogy (SIMCA), hierarchical clustering analysis (HCA), and support vector machine (SVM). Multivariate analysis was performed on the following oil samples: soybean biodiesel, corn biodiesel, diesel S10, and fuel blends prepared from 0% to 100% (v/v) of biodiesel content. All multivariate statistical techniques were able to discriminate between the oil source and the ester percentage in the mixture. It was possible to develop robust multivariate models associated with the FTIR to allow for simultaneous discrimination of the types of oils used for biodiesel production and their content in fuel blends.

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