A new simplex chemometric approach to identify olive oil blends with potentially high traceability

N. Semmar 1, 2, * S. Laroussi-Mezghani 3 N. Grati-Kamoun 4 M. Hammami 3 J. Artaud 2
* Corresponding author
1 LR11IPT09 - Laboratoire de Bioinformatique, biomathématiques, biostatistiques (BIMS)
2 LISA - Laboratoire d'Instrumentation et Sciences Analytiques
3 Faculté de Médecine de Monastir [Tunisie]
4 Institut de l'olivier de Sfax
Abstract : Olive oil blends (OOBs) are complex matrices combining different cultivars at variable proportions. Although qualitative determinations of OOBs have been subjected to several chemometric works, quantitative evaluations of their contents remain poorly developed because of traceability difficulties concerning co-occurring cultivars. Around this question, we recently published an original simplex approach helping to develop predictive models of the proportions of co-occurring cultivars from chemical profiles of resulting blends (Semmar & Artaud, 2015). Beyond predictive model construction and validation, this paper presents an extension based on prediction errors' analysis to statistically define the blends with the highest predictability among all the possible ones that can be made by mixing cultivars at different proportions. This provides an interesting way to identify a priori labeled commercial products with potentially high traceability taking into account the natural chemical variability of different constitutive cultivars.
Keywords : Blend authentication Discriminant analysis Tunisian olive oils Traceability Simplex Optimization Mixture design Fatty acids
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Journal articles
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Submitted on : Wednesday, January 2, 2019 - 9:27:07 AM
Last modification on : Wednesday, July 24, 2019 - 1:18:19 AM

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N. Semmar, S. Laroussi-Mezghani, N. Grati-Kamoun, M. Hammami, J. Artaud. A new simplex chemometric approach to identify olive oil blends with potentially high traceability. Food Chemistry, Elsevier, 2016, 208, pp.150-160. ⟨10.1016/j.foodchem.2016.03.087⟩. ⟨pasteur-01968118⟩

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