Total spectrofluorimetry associated with Principal Component Analysis (PCA) was used to discriminate samples of vegetable oil and animal fat. In addition, a multivariate calibration model was developed that combines spectroflurimetry with Partial Least Squares (PLS) for prediction of concentration of animal fat in mixture with vegetable oil. The multivariate calibration model had an R2 value of 0.98098, which indicates the accuracy of the model. This method has potential application in the control of quality of raw material for production of biodiesel. The control of the concentration of animal fat is important because animal fat is more susceptible to oxidation than vegetable oil. Furthermore, high concentrations of animal fats may increase electricity costs for biodiesel production due to the high melting points of saturated fats that solidify at room temperature and cause the fouling and clogging of pipes.
In this paper, multivariate calibration models have been developed for determination of common adulterants (kerosene, turpentine and residual oil from fried foods) added to diesel. The samples were analyzed by LED spectrofluorimetry and the multivariate calibration models were developed by Partial Least Squares (PLS). The proposal is suggested as an analytical methodology of low-cost, fast and non-destructive able to quantify the presence of contaminants in the diesel. The results showed that adulterants concentrations were adequately reproduced by the fluorescence spectral data.
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