Detection and quantification of olive oil adulteration using fluorescence spectroscopy and chemometric tools

Abstract

The increasing incidence of fraud in the olive oil market, particularly through adulteration with cheaper oils, poses a major challenge for the industry. This study examines the effectiveness of fluorescence spectroscopy combined with chemometric tools as a method to detect and quantify olive oil adulteration. Chemical analyses were used to measure parameters such as specific absorption coefficients K232 and K270, peroxide value, and acidity, in accordance with International Olive Council (IOC) standards. These measurements were conducted on samples of olive oil blended with varying percentages of adulterating oils. 3D fluorescence spectra were analyzed to examine the changes induced by adulteration. The results show that free acidity and peroxide value decrease with increasing adulteration degree, while the parameters K232 and K270 increase with the degree of adulteration. Principal component analysis (PCA) was effectively used to differentiate samples based on the percentage of adulterant. A partial least squares (PLS) regression model was developed, achieving a correlation coefficient of 0.999 for predicting the percentage of adulterant in olive oil. This PLS model also proved effective in predicting oil quality parameters such as free acidity and K232 and K270 indices. The results of this study demonstrate the significant potential of fluorescence spectroscopy as a rapid and non-destructive method for olive oil authentication. PCA emerges as a powerful tool for characterizing pure oils without requiring sample preparation or destruction. Furthermore, PLS models provide accurate means to predict both the presence of adulterants and olive oil quality parameters. This approach offers a promising solution to combat fraud in the olive oil industry, ensuring product integrity and quality.