Quantification of volatile fraction by HS-SPME-GC-MS and sensory evaluation of more than 1200 Virgin Olive Oil samples to support Panel Test in Virgin Olive Oil classification

Extra virgin olive oil (EVOO) is considered as the highest quality product among the edible oils thanks to its pleasant taste and smell and the health properties given by the high content of phenolic compounds. Virgin olive oils, only obtained by physical-mechanical methods, can be classified as extra virgin olive oil (EVOO), virgin olive oil (VOO) or lampante virgin olive oil (LVOO), based on their chemical and sensorial characteristics [1]. To date, the official method for assessing the sensorial properties is the Panel Test, which is carried out by a panel of 8-12 trained tasters and a head panel. This analysis suffers of some drawbacks due to the use of humas, as emotionality, subjectivity, low reproducibility and high costs [2]. For these reasons, to date it is considered increasingly necessary to have available a robust and reliable method for support panel test in virgin olive oil classification, only based on chemical analysis and chemometric tools [3]. Aim of this work is to propose an objective tool for supporting panel test in virgin olive oil classification through predictive models able to correlate chemical and organoleptic properties of more than 1200 virgin olive oil samples. Furthermore, we aimed at getting further light on the molecules able to discriminate between the different categories of samples. To this aim, we analyzed the volatile fraction of more than 1200 virgin olive oil samples by a recently validated HS-SPME-GC-MS method [1], which allowed us quantifying up to 73 volatile organic compounds using 9 internal standard for area normalization. Sensorial characteristics of the same samples were then assessed by the Carapelli’s Panel Test, acknowledged by Italian Ministry. At the same time, oils with different fatty acid composition were stored for six months in several non-accelerated oxidative conditions and periodically analyzed Suitable statistical approaches have been applied in order to create models able to support panel test in virgin olive oil classification. We considered as a key-factor working with a high number of samples in order to have very robust statistical models; the selected samples were almost all EVOOs or VOOs with only a little number of LVOOs. The proposed approaches were mainly aimed at discriminating between EVOO and defective samples. The capability in discriminating between samples defective for different kinds of defects was also evaluated. After quantifying 73 volatile organic compounds for more than 1200 virgin olive oil samples, the obtained data were analyzed together with the sensorial data using statistic tools as t-test and Principal Component Analysis (PCA) for reducing the dimensionality of the data, and Linear Discriminant Analysis (LDA) to find combinations of variables and finding a linear fit able to separate categories of samples. All the proposed approaches resulted able to predict correctly the category of approx. 80% of samples. The main defect of almost all the defective samples was correctly identified. Finally, VOCs able to discriminating between different categories were identified and resulted the same identified in in samples stored in non-accelerated oxidative conditions.