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.

通过 UV 吸收和总荧光光谱学系统地研究了在加速储存条件下特级初榨橄榄油 (EVOO) 的氧化。本研究收集了大量数据，并提出了一种基于机器学习应用于高度聚合数据的监测油质量的方法。研究表明，荧光光谱学具有监测氧化效应和评估 EVOO 质量的能力，即使数据高度聚合也可以使用提出的方法进行。结果显示，利用荧光光谱学不需要复杂的实验室设备，成本效益高的解决方案可以由非科学家在野外使用，提供易于获取的 EVOO 质量评估。

揭示特级初榨橄榄油的老化过程：利用荧光光谱和机器学习技术探究温度对其影响

Shedding Light on the Ageing of Extra Virgin Olive Oil: Probing the  Impact of Temperature with Fluorescence Spectroscopy and Machine Learning  Techniques