A method to detect fraudulent mixtures of extra virgin olive oil

Resum

Motivation: Extra virgin olive oil (EVOO) is a natural juice of the highest quality olives obtained exclusively by mechanical and physical processes at low temperatures. EVOO is one of the most important elements of the Mediterranean diet and it has exceptional sensory and nutritional properties that provide a high economic value. This product is therefore at risk to being adulterated with cheaper edible vegetable oils, which represents an economic fraud. Currently, there is a high interest in establishing a relation between EVOO with its production area due to the European Union and the will to introduce the protected designation of origin (PDO) [1]. In this context, the main objective of this work is to develop a powerful chemical-mathematic method to easily certify the PDO of EVOO samples. Methods: The color of extra virgin olive oil comes from the pigments present in the fruit, which are divided into chlorophylls and carotenoids. EVOO oxidation causes a cascade of physicochemical changes affecting the chromophores of the pigments, and this can be monitored by optical techniques, such as UV-vis spectroscopy and refractometry. The information offered by these methodologies has been linked with the quality and authenticity of EVOO, based on the biological properties of these pigments [2,3]. The refractive index (RI) is characteristic, within certain limits, for each edible oil type, and, therefore, it is an indicator of its purity. In this work, two PDO EVOO types, a non-PDO EVOO and their binary mixtures have been prepared and analyzed. The UV-Vis absorption and RI measurements of these samples have been used to design linear models that allow us to distinguish the proportion of the every EVOO in their mixtures. Results: The data obtained by the UV-visible absorption and RI measurements allow differentiating the composition of the binary mixtures of EVOOs. The absorption espectrum data enables the creation of linear multiparametic regression models. These are based on the measurements of two absorption peaks for each PDO EVOO types. With this information, decent statistical results have been obtained (R2= 0,916; error= 2,9). On the other hand, RI data allow us to distinguish the fraudulent mixtures of PDO a non-PDO EVOO samples without misclassifications. Conclusions: This projects presents a quick, inexpensive and easy method to detect fraudulent mixtures of PDO and non-PDO EVOO. It does not require prior sample preparation nor qualified personnel.