Shedding light on the ageing of extra virgin olive oil: Probing the impact of temperature with fluorescence spectroscopy and machine learning techniques

• DOI: 10.1016/j.lwt.2023.115679
• Publication Date: 2023-12-27T23:30:21Z
• Authors: Francesca Venturini; Silvan Fluri; Manas Mejari; Michael Baumgartner; Dario Piga; Umberto Michelucci
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; Absorption spectroscopy; Machine learning; Oxidation; 006: Spezielle Computerverfahren; Fluorescence spectroscopy; Olive oil; Machine Learning (cs.LG); 540: Chemie

This work systematically investigates the oxidation of extra virgin olive oil (EVOO) under accelerated storage conditions with UV absorption and total fluorescence spectroscopy. With large amount data collected, it proposes a method to monitor oil's quality based on machine learning (ML) applied highly-aggregated data. EVOO is high-quality vegetable that has earned worldwide reputation for its numerous health benefits excellent taste. Despite outstanding quality, degrades over time due oxidation, which can affect both qualities flavour. Therefore, highly relevant quantify effects develop methods assess be easily implemented field conditions, rather than in specialized analytical laboratories. The ML approach indicates two excitation wavelengths (480 nm) (300 exhibit maximum relative change intensity during ageing majority oils, thus identifying are more informative prediction. Also, paper prediction using Such interest because paves way realization low-cost, portable device in-field control. following study demonstrates spectroscopy capability effect EVOO, even when aggregated. It shows complex laboratory equipment not necessary exploit proposed cost-effective solutions, used by non-scientists, could provide an easily-accessible assessment EVOO.