The approach proposed here is focused on the separation of light fractions (corresponding to fatty acids with molecular chains between 8 to 14 carbons) from the fatty acid methyl esters (FAMES) of the biodiesel obtained by the reaction of transesterification of triacylglycerols in the kernel gueiroba oil (Syagrus oleracea) with methanol. The whole biodiesel was fractionated through atmospheric distillation in a single glass column with thermal insulation. Such a separation produced 59.79% in volume of light biodiesel (LB), which was mixed with the standard mineral Jet-A1 kerosene (cf. ASTM, corresponding to the QAV-1, cf. the Brazilian standard of the ANP) for aviation, in the volumetric ratios LB:Jet-A1 2:98; 5:95; 10:90 and 20:80. The values of density, water content, distillation analysis, flash point, calorific value and freezing point were carefully checked for their compliance with the official recommendations for jet fuel. It was found that the mixtures richest in the Jet-A1 mineral kerosene, that is, those containing no more than 5% LB by volume, well meet the recommended standards and are technologically viable to replace pure Jet-A1 kerosene for the propulsion of turbine aircraft.

Two methodologies were developed to monitor the biodiesel content of mafurra in mixtures with diesel using hydrogen nuclear magnetic resonance (1H NMR) Spectroscopy combined with the multivariate regression by orthogonal projections to latent structure (OPLS) and partial least squares (PLS). The efficiency of these methodologies was analyzed based on the figures of merit and the fit of the models through the correlation of the measured and predicted values of the calibration and prediction sets. The results of the figures of merit in the OPLS model were better than in the PLS model. A high correlation between the measured and predicted values was evident in the OPLS model, with a correlation coefficient (R2) greater than 0.99, demonstrating a better fit of the OPLS model in relation to the PLS model which presented a correlation coefficient (R2) less than 0.98. The OPLS model is more robust and has good predictive capacity than the PLS model because it obtained a higher Q 2 value. The excellent results of the application of 1H NMR spectroscopy combined with multivariate regression by OPLS suggest that this analytical methodology is ideal, feasible, efficient and suitable for use by inspection agencies to control the quality of this fuel.

Mid-Infrared absorption spectroscopy associate with the Partial Least Squares regression is the official method to monitor Brazilian commercial diesel quality. This method, however, uses solvents and a large number of samples for the construction of the calibration curves, which generates waste and increases the time needed for the analysis. In order to develop a non-destructive method, being possible to recover the sample after its quantification, decrease the quantity of samples and make use of a single calibration curve, in this study we used the oilseed crambe, which is not used in human food, for the methyl biodiesel production and PLS analysis for their content determination in mixtures with diesel at a concentration range of 1.00 to 30.00 (% v/v). The global model for crambe methyl biodiesel obatined RMSEC = 0.26 (% v/v), RMSECV = 0.35 (% v/v) and RMSEP = 0.41 (% v/v). Complementary, variable selection method iPLS was applied in the global model in order to reduce the spectral range required to regression construction and to improve the RMSEP, RMSECV and RMSEC values.

This work aimed at employing partial least square discriminant analysis (PLS2-DA), allied to mid-infrared (MIR) spectroscopy as an analytical method for simultaneous classification of biodiesels from different oils (soybean and used frying oil) and routes (methylic and ethylic). The evaluation of the model was verified through values of sensitivity and specificity for each parameter, in the interest class. PLS2-DA model showed 100% correct classification in the discrimination of types of biodiesels. Therefore, the proposed methodology is fast, because it allows simultaneous classification of different types of biodiesels. Consequently, it can be used in quality control of this type of biofuel.