Intelligent modelling to monitor the evolution of quality of extra virgin olive oil in simulated distribution conditions

One of the main attributes of extra virgin olive oil (EVOO) is that it possesses multiple advantages for human health, which makes quality control of this functional food an attractive action. In the present research, samples of two different EVOOs (Almazara del Ebro and As Pontis) were held under three different temperature conditions (3 degrees C, 40 degrees C, and room temperature (-23 degrees C)). These different temperatures, as well as time, led to an alteration in the properties of the samples that were studied with visible spectroscopy and multilayer perceptrons (MLPs), which are non-linear mathematical tools. The absorption peaks representing the chlorophylls and carotenoids present in EVOO decrease with time and temperature. Generally, the results show that higher temperatures contribute more to the degradation of EVOO when compared to lower ones. The obtained information was used to design and optimise two MLPs. These tools were able to properly distinguish the time and temperature conditions that EVOO samples underwent. This technique is fast, user-friendly, inexpensive, and non-destructive, so it could be of great use for real-time quality control of olive oils during, for example, their storage and distribution, as ideal conditions could be potentially found. (C) 2018 IAgrE. Published by Elsevier Ltd. All rights reserved.