Abstract

This data set contains the underlying data of the scientific publication: Barbieri S. et al., 2020. FGC in tandem with chemometrics: a rapid screening tool of quality grades of virgin olive oils. This publication will be submitted to a scientific journal. This research aims to develop a classification model based on an untargeted elaboration of volatile fraction fingerprints of virgin olive oils (n=331) analyzed by Flash Gas-Chromatography in order to predict the commercial category of samples (extra virgin olive oil, EVOO; virgin olive oil, VOO; lampante olive oil, LOO). The raw data related to volatile profiles were considered as independent variables, while the quality grades provided by the sensory assessment were defined as reference parameter. This data matrix was elaborated using a linear technique, Partial Least Squares-Discriminant Analysis (PLS-DA), applying, in sequence, two classification models with two categories (EVOO vs noEVOO followed by VOO vs LOO and LOO vs noLOO followed by VOO vs EVOO). Results from this large set of samples provide satisfactory results in terms of percentages of correctly classified samples, ranging from 72 to 85%, in external validation. This confirms the reliability of this approach as rapid screening of quality grades and that it represents a solution for supporting the sensory panels, increasing the efficiency of the controls, applicable also to the industrial sector. This data set contains the raw data obtained from the analysis of all the sample by Flash Gas-Chromatography and used for the building of the PLS-DA data matrix.