README file
Data Set Title: “OLEUM Project. Classification of virgin olive oils based on quality grades and presence of defects using HS-GC-IMS as a rapid screening tool.”
Data Set Authors: Filippo Panni (University of Bologna); Enrico Casadei (University of Bologna); Enrico Valli (University of Bologna); Sara Barbieri (University of Bologna); Chiara Cevoli (University of Bologna); Alessandra Bendini (University of Bologna); Diego L. García González (Instituto de la Grasa - CSIC); Tullia Gallina Toschi (University of Bologna).
Data Set Contact Person: Alessandra Bendini, http://orcid.org/0000-0002-6515-5519, alessandra.bendini@unibo.it.
Data Set License: this data set is distributed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, https://creativecommons.org/licenses/by/4.0/.
Publication Year: 2019
Project Info: OLEUM (Advanced solutions for assuring the overall authenticity and quality of olive oil), funded by European Union, Horizon 2020 Programme. Grant Agreement num. 635690; http://www.oleumproject.eu/.  
Data set contents
The data set consists of:
•	Two numerical quantitative files, each one saved in .xlsx and .ods formats 
o	“OLEUM_AnalysisOfVolatileCompoundsInVOObyHS-GC-IMS.xlsx”
“OLEUM_AnalysisOfVolatileCompoundsInVOObyHS-GC-IMS.ods”
o	“OLEUM_DataMatrix_AnalysisOfVolatileCompoundsInVOObyHS-GC-IMS.xlsx”*
“OLEUM_DataMatrix_AnalysisOfVolatileCompoundsInVOObyHS-GC-IMS.ods”*
* These files cannot be openly shared because they contain data that are in course of elaboration by other statistical approaches, with the aim of possibly publishing the results and exploiting them. They will be made available only upon motivated request.
•	a README file saved in .rtf format 
“OLEUM_AnalysisOfVolatileCompoundsInVOObyHS-GC-IMS_README.rtf”
Data set Documentation
Abstract
This data set contains the underlying data of the scientific publication:
Valli, E.; Panni, F.; Casadei, E.; Barbieri, S.; Cevoli, C.; Bendini, A.; García-González, D.L.; Gallina Toschi, T. An HS-GC-IMS Method for the Quality Classification of Virgin Olive Oils as Screening Support for the Panel Test. Foods 2020, 9(5), 657; https://doi.org/10.3390/foods9050657.    
Sensory analysis, carried out by Panel test, is essential for the quality classification of virgin olive oils (VOOs). The presence and perceived intensity of fruity attribute and sensory defects are linked with the occurrence of specific volatile compounds. Instrumental screening methods based on analysis of volatiles can support the Panel test through fast pre-classification of samples with a known probability, thus increasing the efficiency of quality control. 
A Headspace Gas Chromatography Ion Mobility Spectrometer (HS-GC-IMS) was used to analyze commercial VOOs by a semi-targeted approach. PLS-DA models were built by data matrices composed of 15 selected volatile compounds with which was possible to classify the samples based on quality grades and presence of defects. 
Content of the files 
•	OLEUM_AnalysisOfVolatileCompoundsInVOObyHS-GC-IMS.xlsx / .ods
o	tab Samples category contains the quality grade of each samples.
o	tab Linearity contains parameters considered for the evaluation of the linearity of the volatile compounds in SMA (from compound 1 to compound 7) and SMB (from compound 8 to compound 15).
o	tab Repeatability contains values expressed as the mean of the relative standard deviation (RSD%) for the selected compounds.
o	tab Y data for PLSDA_quality grades contains the Y references values used to build the PLS-DA quality grades models.
o	tab PLS-DA_quality grades contains percentages of correctly classified samples by the 4 PLS-DA models for the quality grade classification of virgin olive oils (EVOO vs no-EVOO; LOO vs no-LOO; VOO vs LOO; EVOO vs VOO).
o	tab Y data for PLSDA_defects contains the Y references values used to build the PLS-DA defects models.
o	tab PLS-DA_defects contains percentages of correctly classified samples by the 3 PLS-DA models to determine the presence of defects in virgin olive oils (Musty vs no-Musty; Rancid vs no-Rancid; Fusty-Muddy vs no-Fusty-Muddy).
•	OLEUM_DataMatrix_AnalysisOfVolatileCompoundsInVOObyHS-GC-IMS.xlsx/.ods 
tab X data matrix for PLS-DA contains the X values used to build the PLS-DA models.
File specifics
To create the .ods files, “LibreOffice Calc” version: 6.3.6.2 was used.
List of variables 
Acronyms:
•	Head Space - Gas Chromatography – Ion Mobility Spectrometry (HS-GC-IMS);
•	Extra Virgin Olive Oil (EVOO);
•	Virgin Olive Oil (VOO);
•	Lampante Olive Oil (LOO);
•	Regression coefficient (R2);
•	Relative Standard Deviation (RSD);
•	Drift time (Dt);
•	Retention time (Rt);
•	Partial Least Squares – Discriminant Analysis (PLS-DA);
•	Standard Matrix A (SMA);
•	Standard Matrix B (SMB).