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The most common adulterants found in extra-virgin olive oil are refined olive oil and other vegetable oils, such as sunflower, soybean, corn, and canola. In addition to constituting economic fraud, adulteration can cause serious damage to the health of the consumer. This study focuses on the detection and quantification of the adulteration of extra-virgin olive oil with edible oils, using spectrofluorimetry and chemometrics. The data were analyzed by Principal Components Analysis (PCA) and Partial Least Squares (PLS) analysis. Through PCA, it was possible to separate the samples into two distinct areas, olive oil and other edible oils, based on their chemical composition. The PLS model, built with the spectra of mixtures of soybean oil in extra-virgin olive oil, exhibited an R 2 of 0.99412 and low RMSEP (Root Mean Square Error of Prediction) (3.59), RMSEC (Root Mean Square Error of Calibration) (2.32) and bias (4.77. 10 -7 ) values. Thus, the PLS model was considered exact for calibration and prediction.

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.