  This work systematically investigates the oxidation of extra virgin olive oil
(EVOO) under accelerated storage conditions with UV absorption and total
fluorescence spectroscopy. With the large amount of data collected, it proposes
a method to monitor the oil's quality based on machine learning applied to
highly-aggregated data. EVOO is a high-quality vegetable oil that has earned
worldwide reputation for its numerous health benefits and excellent taste.
Despite its outstanding quality, EVOO degrades over time owing to oxidation,
which can affect both its health qualities and flavour. Therefore, it is highly
relevant to quantify the effects of oxidation on EVOO and develop methods to
assess it that can be easily implemented under field conditions, rather than in
specialized laboratories. The following study demonstrates that fluorescence
spectroscopy has the capability to monitor the effect of oxidation and assess
the quality of EVOO, even when the data are highly aggregated. It shows that
complex laboratory equipment is not necessary to exploit fluorescence
spectroscopy using the proposed method and that cost-effective solutions, which
can be used in-field by non-scientists, could provide an easily-accessible
assessment of the quality of EVOO.
