E-Nose Quality Evaluation of Extra Virgin Olive Oil Stored in Different Containers

The degradation process of virgin olive oil (VOO) is related to storage time and the type of storage container used. The aim of this work is to explore the evolution of the VOO quality stored in different container types over a defined storage period in order to predict the organoleptic characteristics using a non-destructive technique such as the electronic-nose (E-nose). The Picual variety VOO was stored in different containers over a period of 21 months and monitored using sensory analysis, volatile compounds, and an E-nose. The panelists showed that oil stored in dark glass bottles and in green polyethylene bottles began to show defects after 12 and 15 weeks, respectively. However, oil stored in tin containers retained its quality throughout the 21 months studied. A total of 31 volatile compounds were identified, and the evolution of the volatile profile in the different containers during the storage period was studied. The E-nose data were able to classify oil quality by container using principal component analysis (PCA). Furthermore, the E-nose data combined with partial least squares (PLS) regression enabled the building of a predictive model to quantify sensory defect values (R-CV(2) = 0.92; R-CV(2) = 0.86), evidencing that this technique would be an appropriate screening tool to support a sensory panel.

Automated summary

The aim of this study was to explore the evolution of the quality of virgin olive oil stored in different containers over a defined period of time using a non-destructive technique, the electronic-nose (E-nose), to predict its organoleptic characteristics. The results showed that oil stored in dark glass bottles and green polyethylene bottles started showing defects after 12 and 15 weeks, respectively, while oil stored in tin containers maintained its quality throughout the 21-month storage period. A total of 31 volatile compounds were identified and the volatile profiles in different containers during storage were studied. The E-nose data, combined with partial least squares (PLS) regression, were able to build a predictive model to quantify sensory defect values, indicating its potential as a suitable tool to support sensory evaluation.