7^th European Food Safety & Standards Conference

Extra virgin olive oil (EVOO) is known for its beneficial properties in nutrition, making the quality control of this food a relevant task. In this regard, three different high-quality grade Spanish EVOOs (As Pontis, Marqués de Valdueza, and Empeltre), which are currently exported to the USA, were monitored under three different temperature conditions (3ºC, 40ºC, and room temperature) in order to simulate potential situations which EVOO undergoes during shipment and storage. The different temperatures led to distinct modifications of the optical properties of the EVOO samples, which were analyzed by means of absorption visible spectroscopy and neural network models. These modifications also depended on the time span in which the EVOO withstood a given temperature. The final algorithm is a non-linear mathematical tool that has been employed to relate the UV-vis absorption with temperature and exposure time. The intensity of the UV-vis absorption spectra peaks, which are originated by the chlorophylls and carotenoids present in EVOO, decrease with time and temperature. Generally, the results reveal that higher temperatures lead to a faster degradation of EVOO when compared to lower ones. This data was utilized to design, optimize, and validate an artificial neural network, capable of distinguishing the time and temperature conditions that EVOO samples suffered. This technique is fast, user-friendly, cost-effective, as well as non- destructive, meaning that it could have a direct impact for the real-time quality control of edible oils during their distribution chain, as ideal conditions could be found and implemented.