Journal of Food Processing & Technology

Quality Estimation of Extra Virgin Olive Oil During Shipment and Storage

Reinventing the Food & Beverage Industry

May 09-10, 2016 New Orleans, USA

Jose S Torrecilla, Regina Aroca-Santos, John C Cancilla, Enrique S Pariente, Gemma Matute, John D Cancilla and Kacper W Wierzchos

Complutense University of Madrid, Spain
Cherrygate S.L., Spain
University of South Florida, USA

Posters & Accepted Abstracts: J Food Process Technol

Abstract:

One of the main properties of extra virgin olive oil (EVOO) is that it possesses multiple advantages for human health, which turns the quality control of this functional food into a great necessity. In the present research, samples of 3 different Spanish EVOOs (Marqués de Valdueza, Empeltre and As Pontis), which are currently being exported to USA, were held under 3 different temperature conditions (3ºC, 40ºC and room temperature) simulating some of the possible conditions that EVOO suffers during shipment and storage. The consequences of the different temperatures, as well as time, led to an alteration of the properties of the EVOO samples, which were studied by means of absorption visible spectroscopy and neural network (NN) modeling, which is a nonlinear mathematical tool that has been used to relate the absorption with time and temperature. The absorption peaks representing the chlorophylls and carotenoids present in EVOO decrease with time and temperature. Generally, the results showed that higher temperatures contribute more in the degradation of EVOO when compared to lower ones. The obtained information was used to create, design, and optimize a neural network, which is able to fairly distinguish the time and temperature conditions that EVOO samples underwent. This technique is fast, user-friendly, and non-destructive, so it could be of great use for the real-time quality control of edible oils during, for example, their distribution chain, as ideal conditions could be potentially optimized.

Biography :

Email: jstorre@quim.ucm.es