Rheological analysis for detection of extra virgin olive oil adulteration with vegetable oils: predicting oil type by artificial neural network

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T. Dursun Capar
H. Kavuncuoglu
H. Yalcin
G. Toga


extra virgin olive oil, vegetable oils, adulteration, rheology, artificial neural network


Food adulteration is a major concern in the food industry. High prices and increasing demand have made the adulteration of extra virgin olive oil (EVOO) a major concern for consumers. The purpose of this study was to detect EVOO adulteration by using rheological parameters. EVOO adulteration was identified with the addition of different types of vegetable oils (hazelnut, sunflower, and canola) at a ratio 25, 50 and 75% by weight. Refractive index (RI; 20 °C) and fatty acid composition of oils were also measured. Dynamic and steady rheological tests were managed. RI value of the EVOO was 1.4698. Addition of different vegetable oils increased the RI of the blended samples. Steady and dynamic test results indicated that EVOO adulteration can be detected by rheological tests. Also, ?, G' and G" were used to verify the adulteration of EVOO with different types of vegetable oils by using artificial neural network (ANN). The developed ANN was able to reveal the relationship between ?, G' and G" and studied oil type. Results show that ANN achieved a satisfactory prediction for vegetable oil adulteration of the studied oil type. This study provides a valuable insight to a method that allows the detection of extra olive oil adulteration in a more time and cost-efficient way.

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