TY - JOUR
T1 - Discrimination of Tunisian and Italian extra-virgin olive oils according to their phenolic and sterolic fingerprints
AU - Mohamed, Mbarka Ben
AU - Rocchetti, Gabriele
AU - Montesano, Domenico
AU - Ali, Sihem Ben
AU - Guasmi, Ferdaous
AU - Grati-Kamoun, Naziha
AU - Lucini, Luigi
PY - 2018
Y1 - 2018
N2 - In the last years, olive oil authentication issues have become topics of prominent importance, not only for consumers, but also for suppliers, retailers, and administrative authorities, and particularly for assurance of public health. In this work, the sterolic and phenolic profile of Tunisian and Italian extra-virgin olive oil (EVOO) samples was depicted using an untargeted UHPLC-ESI/QTOF mass spectrometry approach. Polyphenols and sterols were quantified according to their chemical sub-classes, with high sterols (around 1000 up to 2000 mg/ kg) and tyrosols (on average 420.2 mg/kg) contents detected. The metabolomics data were elaborated by means of multivariate statistics, i.e. unsupervised hierarchical cluster analysis and orthogonal projections to latent structures discriminant analysis (OPLS-DA). This approach allowed identifying the best markers (i.e. hydroxybenzoic acids, cholesterol and stigmasterol derivatives) of the geographical origin able to discriminate Tunisian and Italian EVOO samples, showing the potential of sterolic and phenolic fingerprints for olive oil authenticity evaluations.
AB - In the last years, olive oil authentication issues have become topics of prominent importance, not only for consumers, but also for suppliers, retailers, and administrative authorities, and particularly for assurance of public health. In this work, the sterolic and phenolic profile of Tunisian and Italian extra-virgin olive oil (EVOO) samples was depicted using an untargeted UHPLC-ESI/QTOF mass spectrometry approach. Polyphenols and sterols were quantified according to their chemical sub-classes, with high sterols (around 1000 up to 2000 mg/ kg) and tyrosols (on average 420.2 mg/kg) contents detected. The metabolomics data were elaborated by means of multivariate statistics, i.e. unsupervised hierarchical cluster analysis and orthogonal projections to latent structures discriminant analysis (OPLS-DA). This approach allowed identifying the best markers (i.e. hydroxybenzoic acids, cholesterol and stigmasterol derivatives) of the geographical origin able to discriminate Tunisian and Italian EVOO samples, showing the potential of sterolic and phenolic fingerprints for olive oil authenticity evaluations.
KW - Extra-virgin olive oil
KW - Food authentication
KW - Food metabolomics
KW - Multivariate statistics
KW - Polyphenols
KW - Sterols
KW - Extra-virgin olive oil
KW - Food authentication
KW - Food metabolomics
KW - Multivariate statistics
KW - Polyphenols
KW - Sterols
UR - http://hdl.handle.net/10807/122984
UR - http://www.elsevier.com/inca/publications/store/4/2/2/9/7/0
U2 - 10.1016/j.foodres.2018.02.010
DO - 10.1016/j.foodres.2018.02.010
M3 - Article
SN - 0963-9969
VL - 106
SP - 920
EP - 927
JO - Food Research International
JF - Food Research International
ER -