ABSTRACT
Partial last square regression (PLS) and artificial neural network (ANN) combined to FTIR-ATR and FTNIR spectroscopies have been used to design calibration models for the determination of methyl ester content (%, w/w) in biodiesel blends (methyl ester+diesel). Methyl esters were obtained by the methanolysis of soybean, babassu, dende, and soybean fried oils. Two sets of samples have been used: Group I, binary mixtures (diesel+one kind of methyl ester), corresponding to 96 biodiesel blends (0-100%, w/w), and Group II, quaternary mixtures (diesel+three types of methyl esters), corresponding to 60 biodiesel blends (0-100%, w/w). The PLS results have shown that the FTNIR model for Group I is more precise and accurate (+/-0.02 and +/-0.06%, w/w). In the case of Group II the PLS models (FTIR-ATR and FTNIR) have shown the same accuracies, while the ANN/FTNIR models has presented better performance than the ANN/FTIR-ATR models. The best accuracy was achieved by the ANN/FTNIR model for diesel determination (0.14%, w/w) while the worthiest was that of dende ANN/FTIR-ATR model (0.6%, w/w). Precisions in Group II analysis ranged from 0.06 to 0.53% (w/w) and coefficients of variation were better than 3% indicating that these models are suitable for the determination of diesel-biodiesel blends composed of methyl esters derived from different vegetable oils.
