IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v9y2017i8p1471-d108913.html
   My bibliography  Save this article

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

Author

Listed:
  • Chiara Roberta Girelli

    (Department of Biological and Environmental Science and Technologies (Di.S.Te.B.A.), University of Salento, via Prov.le Lecce-Monteroni, 73100 Lecce, Italy)

  • Laura Del Coco

    (Department of Biological and Environmental Science and Technologies (Di.S.Te.B.A.), University of Salento, via Prov.le Lecce-Monteroni, 73100 Lecce, Italy)

  • Francesco Paolo Fanizzi

    (Department of Biological and Environmental Science and Technologies (Di.S.Te.B.A.), University of Salento, via Prov.le Lecce-Monteroni, 73100 Lecce, Italy)

Abstract

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.

Suggested Citation

  • Chiara Roberta Girelli & Laura Del Coco & Francesco Paolo Fanizzi, 2017. "Tunisian Extra Virgin Olive Oil Traceability in the EEC Market: Tunisian/Italian (Coratina) EVOOs Blend as a Case Study," Sustainability, MDPI, vol. 9(8), pages 1-11, August.
  • Handle: RePEc:gam:jsusta:v:9:y:2017:i:8:p:1471-:d:108913
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/9/8/1471/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/9/8/1471/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Elaine Holmes & Ruey Leng Loo & Jeremiah Stamler & Magda Bictash & Ivan K. S. Yap & Queenie Chan & Tim Ebbels & Maria De Iorio & Ian J. Brown & Kirill A. Veselkov & Martha L. Daviglus & Hugo Kesteloot, 2008. "Human metabolic phenotype diversity and its association with diet and blood pressure," Nature, Nature, vol. 453(7193), pages 396-400, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Edoardo Saccenti & Leonardo Tenori & Paul Verbruggen & Marieke E Timmerman & Jildau Bouwman & Jan van der Greef & Claudio Luchinat & Age K Smilde, 2014. "Of Monkeys and Men: A Metabolomic Analysis of Static and Dynamic Urinary Metabolic Phenotypes in Two Species," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-13, September.
    2. Eslami, Seyed Pouyan & Ghasemaghaei, Maryam, 2018. "Effects of online review positiveness and review score inconsistency on sales: A comparison by product involvement," Journal of Retailing and Consumer Services, Elsevier, vol. 45(C), pages 74-80.
    3. Shinji Fukuda & Yumiko Nakanishi & Eisuke Chikayama & Hiroshi Ohno & Tsuneo Hino & Jun Kikuchi, 2009. "Evaluation and Characterization of Bacterial Metabolic Dynamics with a Novel Profiling Technique, Real-Time Metabolotyping," PLOS ONE, Public Library of Science, vol. 4(3), pages 1-10, March.
    4. Singer Burton, 2011. "Genome-Phenome Linkages in Human Population Surveys, with Special Emphasis on the Health and Retirement Survey," Forum for Health Economics & Policy, De Gruyter, vol. 14(3), pages 1-24, April.
    5. William Astle & Maria De Iorio & Sylvia Richardson & David Stephens & Timothy Ebbels, 2012. "A Bayesian Model of NMR Spectra for the Deconvolution and Quantification of Metabolites in Complex Biological Mixtures," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1259-1271, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:9:y:2017:i:8:p:1471-:d:108913. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.