外文摘要：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 (ML) 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 due 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 analytical laboratories. The ML approach indicates that the two excitation wavelengths (480 nm) and (300 nm) exhibit the maximum relative change in fluorescence intensity during the ageing for the majority of the oils, thus identifying the wavelengths which are more informative for quality prediction. Also, the paper proposes a method for the prediction of olive oil quality using highly -aggregated data. Such a method is of interest because it paves the way to the realization of a low-cost, portable device for in -field quality control. 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.
外文关键词：machine learning;Fluorescence spectroscopy;oxidation;quality control;Optical sensor;extra virgin olive oil;UV spectroscopy;Thermal stability
作者：Venturini, Francesca;Michelucci, Umberto;Baumgartner, Michael;Fluri, Silvan;Mejari, Manas;Piga, Dario
作者单位：Zurich Univ Appl Sci;Adv AI Lab;USI SUPSI;Lucerne Univ Appl Sci & Arts
期刊名称：LWT-FOOD SCIENCE AND TECHNOLOGY
期刊影响因子：0.0
出版年份：2024
出版刊次：191
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台