Abstract
New research on Machine Learning is the subject of a report. According to news reporting out of Winterthur, Switzerland, by NewsRx editors, research stated, "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." Financial support for this research came from Hasler Foundation project "ARES: AI for fluoREscence Spectroscopy in oil." Our news journalists obtained a quote from the research from the Zurich University of Applied Sciences, "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."
NETL
NSTL
Elsevier