Rapid direct analysis to discriminate geographic origin of extra virgin olive oils by flash gas chromatography electronic nose and chemometrics

•Multivariate analysis of FGC E-nose data allows a rapid screening of EVOOs.•PCA and PLS-DA of volatile profile give fingerprint of EVOOs geographical origin.•FGC E-nose is comparable with SPME/GC–MS in analyzing volatile profile of EVOOs. At present, the geographical origin of extra virgin olive oi...

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Bibliographic Details
Published in:Food chemistry 2016-08, Vol.204, p.263-273
Main Authors: Melucci, Dora, Bendini, Alessandra, Tesini, Federica, Barbieri, Sara, Zappi, Alessandro, Vichi, Stefania, Conte, Lanfranco, Gallina Toschi, Tullia
Format: Article
Language:English
Subjects:
Chromatography, Gas - methods
Discriminant Analysis
Electronic Nose
Extra virgin olive oil
FGC E-nose
Geographic origin
Headspace volatile compounds
Italy
Least-Squares Analysis
Non-target analysis
Olea
Olea - chemistry
Olive Oil - chemistry
PCA
Plant Oils - chemistry
PLS-DA
SPME/GC–MS
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Summary:•Multivariate analysis of FGC E-nose data allows a rapid screening of EVOOs.•PCA and PLS-DA of volatile profile give fingerprint of EVOOs geographical origin.•FGC E-nose is comparable with SPME/GC–MS in analyzing volatile profile of EVOOs. At present, the geographical origin of extra virgin olive oils can be ensured by documented traceability, although chemical analysis may add information that is useful for possible confirmation. This preliminary study investigated the effectiveness of flash gas chromatography electronic nose and multivariate data analysis to perform rapid screening of commercial extra virgin olive oils characterized by a different geographical origin declared in the label. A comparison with solid phase micro extraction coupled to gas chromatography mass spectrometry was also performed. The new method is suitable to verify the geographic origin of extra virgin olive oils based on principal components analysis and discriminant analysis applied to the volatile profile of the headspace as a fingerprint. The selected variables were suitable in discriminating between “100% Italian” and “non-100% Italian” oils. Partial least squares discriminant analysis also allowed prediction of the degree of membership of unknown samples to the classes examined.
ISSN:0308-8146
1873-7072
DOI:10.1016/j.foodchem.2016.02.131