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Targeted authentication approach for the control of the contamination of refined olive oil by refined seeds oils using chromatographic techniques and chemometrics models

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Abstract

Adulteration of olive oil during the packaging process may take place by accidental contamination. The development of analytical methodologies which enable detection of adulterations is warranted, since the addition of refined vegetable oils to refined olive oil (ROO) at low percentages could be a very challenging task. For this purpose, refined soybean, sunflower, corn and rapeseed oil are used for ROO fraudulent operation. ΔECN42 could be used as a parameter for the detection of ROO fraud with 0.5% refined corn oil (RCO), 0.5% refined soybean oil (RSbO), 0.4% refined rapeseed oil (RRO) and 0.1% refined sunflower oil (RSfO). The global method can be used as an effective parameter in refined olive oil adulteration even at low levels: 0.4% of RCO and RRO, 0.3% of RSbO and 0.1% of RSfO. The sterol composition can be considered as good markers of purity. Linear discriminant analysis could represent the most discriminant variable and fast tool for evaluation of ROO adulteration.

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Abbreviations

ROO:

Refined olive oil

RSbO:

Refined soybean oil

RSfO:

Refined sunflower oil

RCO:

Refined corn oil

RRO:

Refined rapeseed oil

RCoO:

Refined corn oil

FID:

Flame ionization detector

FAME:

Fatty acid methyl ester

GC:

Gas chromatography

TAG:

Triacyglycerol

HPLC:

High performance liquid chromatography

IOC:

International olive council

LDA:

Linear discriminant analysis

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Acknowledgements

The authors would like to acknowledge the support of the Ministère de l’Enseignement Supérieur et de la Recherche Scientifique (LR14ES08) and the Ministère de l’Agriculture (ONH Laboratory-Sfax), Tunisia, to this research work.

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Correspondence to Mohamed Bouaziz.

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Jabeur, H., Drira, M. & Bouaziz, M. Targeted authentication approach for the control of the contamination of refined olive oil by refined seeds oils using chromatographic techniques and chemometrics models. Eur Food Res Technol 247, 2455–2472 (2021). https://doi.org/10.1007/s00217-021-03811-0

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