Tunisian Extra Virgin Olive Oil Traceability in the EEC Market: Tunisian/Italian (Coratina) EVOOs Blend as a Case Study

In order to check the reliability of an NMR-based metabolomic approach to evaluating blend composition (and declaration), a series of 81 Italian/Tunisian blends samples at different percentage composition (from 10/90 to 90/10% Coratina/Tunisian oil by 10% increase step) were prepared starting from f...

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Main Authors: Chiara Roberta Girelli, Laura Del Coco, Francesco Paolo Fanizzi
Format: Article
Language:English
Published: MDPI AG 2017-08-01
Series:Sustainability
Subjects:
Online Access:https://www.mdpi.com/2071-1050/9/8/1471
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spelling doaj-03f18161e4a741d2ae5906f4c06a4d0e2020-11-24T23:20:36ZengMDPI AGSustainability2071-10502017-08-0198147110.3390/su9081471su9081471Tunisian Extra Virgin Olive Oil Traceability in the EEC Market: Tunisian/Italian (Coratina) EVOOs Blend as a Case StudyChiara Roberta Girelli0Laura Del Coco1Francesco Paolo Fanizzi2Department of Biological and Environmental Science and Technologies (Di.S.Te.B.A.), University of Salento, via Prov.le Lecce-Monteroni, 73100 Lecce, ItalyDepartment of Biological and Environmental Science and Technologies (Di.S.Te.B.A.), University of Salento, via Prov.le Lecce-Monteroni, 73100 Lecce, ItalyDepartment of Biological and Environmental Science and Technologies (Di.S.Te.B.A.), University of Salento, via Prov.le Lecce-Monteroni, 73100 Lecce, ItalyIn order to check the reliability of an NMR-based metabolomic approach to evaluating blend composition (and declaration), a series of 81 Italian/Tunisian blends samples at different percentage composition (from 10/90 to 90/10% Coratina/Tunisian oil by 10% increase step) were prepared starting from five Coratina (Apulia) and five Tunisian extra virgin olive oil (EVOO) batches. Moreover, a series of nine binary mixtures blend oils were obtained, starting from the two batches’ oil sums. The models built showed the linear relationship between the NMR signals and the percentage composition of the blends. In particular, a high correlation with the percentage composition of blends was obtained from the partial least squares (PLS) regression model, when the two batches oil sums were used for the binary mixtures of blend samples. These proposed methods suggest that a multivariate analysis (MVA)-based NMR approach—in particular PLS regression (PLSR)—could be a very useful tool (including for trading purposes) to assess quantitative blend composition. This is important for the sustainability of the goods’ free movement, especially in the agrifood sector. This cornerstone policy of current common markets is also clearly linked to the availability of methods for certifying the origin of the foodstuffs and their use in the assembly of final product for the consumer.https://www.mdpi.com/2071-1050/9/8/1471extra virgin olive oilcommercial purpose1H-NMR spectroscopyEVOOs origin assessmentmultivariate statistical analysisPLSRprojections to latent structure by means of partial least squares regression
collection DOAJ
language English
format Article
sources DOAJ
author Chiara Roberta Girelli
Laura Del Coco
Francesco Paolo Fanizzi
spellingShingle Chiara Roberta Girelli
Laura Del Coco
Francesco Paolo Fanizzi
Tunisian Extra Virgin Olive Oil Traceability in the EEC Market: Tunisian/Italian (Coratina) EVOOs Blend as a Case Study
Sustainability
extra virgin olive oil
commercial purpose
1H-NMR spectroscopy
EVOOs origin assessment
multivariate statistical analysis
PLSR
projections to latent structure by means of partial least squares regression
author_facet Chiara Roberta Girelli
Laura Del Coco
Francesco Paolo Fanizzi
author_sort Chiara Roberta Girelli
title Tunisian Extra Virgin Olive Oil Traceability in the EEC Market: Tunisian/Italian (Coratina) EVOOs Blend as a Case Study
title_short Tunisian Extra Virgin Olive Oil Traceability in the EEC Market: Tunisian/Italian (Coratina) EVOOs Blend as a Case Study
title_full Tunisian Extra Virgin Olive Oil Traceability in the EEC Market: Tunisian/Italian (Coratina) EVOOs Blend as a Case Study
title_fullStr Tunisian Extra Virgin Olive Oil Traceability in the EEC Market: Tunisian/Italian (Coratina) EVOOs Blend as a Case Study
title_full_unstemmed Tunisian Extra Virgin Olive Oil Traceability in the EEC Market: Tunisian/Italian (Coratina) EVOOs Blend as a Case Study
title_sort tunisian extra virgin olive oil traceability in the eec market: tunisian/italian (coratina) evoos blend as a case study
publisher MDPI AG
series Sustainability
issn 2071-1050
publishDate 2017-08-01
description In order to check the reliability of an NMR-based metabolomic approach to evaluating blend composition (and declaration), a series of 81 Italian/Tunisian blends samples at different percentage composition (from 10/90 to 90/10% Coratina/Tunisian oil by 10% increase step) were prepared starting from five Coratina (Apulia) and five Tunisian extra virgin olive oil (EVOO) batches. Moreover, a series of nine binary mixtures blend oils were obtained, starting from the two batches’ oil sums. The models built showed the linear relationship between the NMR signals and the percentage composition of the blends. In particular, a high correlation with the percentage composition of blends was obtained from the partial least squares (PLS) regression model, when the two batches oil sums were used for the binary mixtures of blend samples. These proposed methods suggest that a multivariate analysis (MVA)-based NMR approach—in particular PLS regression (PLSR)—could be a very useful tool (including for trading purposes) to assess quantitative blend composition. This is important for the sustainability of the goods’ free movement, especially in the agrifood sector. This cornerstone policy of current common markets is also clearly linked to the availability of methods for certifying the origin of the foodstuffs and their use in the assembly of final product for the consumer.
topic extra virgin olive oil
commercial purpose
1H-NMR spectroscopy
EVOOs origin assessment
multivariate statistical analysis
PLSR
projections to latent structure by means of partial least squares regression
url https://www.mdpi.com/2071-1050/9/8/1471
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