Interregional traceability of Tunisian olive oils to the provenance soil by multielemental fingerprinting and chemometrics. (15th June 2019)

• Record Type: Journal Article
• Authors: Damak, Fadwa; Asano, Maki; Baba, Koji; Suda, Aomi; Araoka, Daisuke; Wali, Ahmed; Isoda, Hiroko; Nakajima, Mitsutoshi; Ksibi, Mohamed; Tamura, Kenji
• Is Part Of: Food chemistry. Volume 283(2019)
• Page Start: 656
• Page End: 664
• Publication Date: 2019-06-15
• Subjects: Sodium (PubChem CID: 5360545) -- Magnesium (PubChem CID: 888) -- Iron (PubChem CID: 23925) -- Zinc (PubChem CID: 32051) -- Strontium (PubChem CID: 104798) -- Manganese (PubChem CID: 23930) -- Barium (PubChem CID: 104810) -- Lead (PubChem CID: 5352425) -- Vanadium (PubChem CID: 5460753) -- Rubidium (PubChem CID: 105153); Olive oil -- Soil -- ICP-MS -- Geographical traceability -- Geochemical fingerprints; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2019.01.082
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: ICP-MS fingerprinting tool with chemometric pattern recognition technique was used. Reference values for elements in Tunisian olive oils were created. The suitability of multielements in geographic authentication of olive oils was proven. Multielements explained the oils' geographical origin rather than varietal origin. The oils are linked to the provenance soils by inherited geochemical fingerprints. Abstract: The aim of this study was to prove the usefulness of multielements as provenance markers of olive oils by evaluating their link with soil composition and their discriminatory power. Eleven elements in twenty-one olive oils and their paired soils from four Tunisian regions were characterized. Chemometrics have been implemented for ICP-MS data processing. Principal component analysis identified the predominant geochemical source of the elements in the oils based on their associations according to Goldschmidt's rule. Although a clear correlation was not proven, correspondence was identified between the discriminating elements for both the soils and olive oils, which included Fe, Rb, Mg, and Pb. Linear discriminant analysis achieved classification and prediction rates of 92.1% and 87.3%, respectively. Our study substantiates the validity of multielements as markers of the olive oils' provenance, and that an elemental fingerprinting approach can be successfully applied in the construction of a database of Tunisian olive oils.