Extra-virgin olive oil (EVOO) is subjected to different frauds. This work aimed at integrating the untargeted phenolic and sterol signatures with supervised multivariate discriminant analysis (OPLS-DA) and Artificial Neural Networks (ANN) for tracing the authenticity (as a function of variety, origin, and the blending) of Taggiasca Ligure, a renowned Italian EVOO. Overall, 408 samples from three consecutive growing seasons were used. Despite the cultivar, season, growth altitude, and geographical origin were all contributing to phytochemical profile, OPLS-DA models allowed identifying specific markers of authenticity. Cholesterol-derivatives and phenolics (tyrosols and oleuropeins, stilbenes, lignans, phenolic acids, and flavonoids) were the best markers, based on statistics. Thereafter, ANN was used to discriminate authentic Taggiasca, and the sensitivity was 100% (32/32) thus indicating an excellent classification. Our results strengthen the concept of “terroir” for EVOO and indicate that profiling sterols and phenolics can support EVOO integrity if adequate data treatments are adopted.

Senizza, B., Ganugi, P., Trevisan, M., Lucini, L., Combining untargeted profiling of phenolics and sterols, supervised multivariate class modelling and artificial neural networks for the origin and authenticity of extra-virgin olive oil: A case study on Taggiasca Ligure, <<FOOD CHEMISTRY>>, 2022; 404 (404): N/A-N/A. [doi:10.1016/j.foodchem.2022.134543] [https://hdl.handle.net/10807/232216]

Combining untargeted profiling of phenolics and sterols, supervised multivariate class modelling and artificial neural networks for the origin and authenticity of extra-virgin olive oil: A case study on Taggiasca Ligure

Senizza, Biancamaria;Ganugi, Paola;Trevisan, Marco;Lucini, Luigi
2023

Abstract

Extra-virgin olive oil (EVOO) is subjected to different frauds. This work aimed at integrating the untargeted phenolic and sterol signatures with supervised multivariate discriminant analysis (OPLS-DA) and Artificial Neural Networks (ANN) for tracing the authenticity (as a function of variety, origin, and the blending) of Taggiasca Ligure, a renowned Italian EVOO. Overall, 408 samples from three consecutive growing seasons were used. Despite the cultivar, season, growth altitude, and geographical origin were all contributing to phytochemical profile, OPLS-DA models allowed identifying specific markers of authenticity. Cholesterol-derivatives and phenolics (tyrosols and oleuropeins, stilbenes, lignans, phenolic acids, and flavonoids) were the best markers, based on statistics. Thereafter, ANN was used to discriminate authentic Taggiasca, and the sensitivity was 100% (32/32) thus indicating an excellent classification. Our results strengthen the concept of “terroir” for EVOO and indicate that profiling sterols and phenolics can support EVOO integrity if adequate data treatments are adopted.
2023
Inglese
Senizza, B., Ganugi, P., Trevisan, M., Lucini, L., Combining untargeted profiling of phenolics and sterols, supervised multivariate class modelling and artificial neural networks for the origin and authenticity of extra-virgin olive oil: A case study on Taggiasca Ligure, <<FOOD CHEMISTRY>>, 2022; 404 (404): N/A-N/A. [doi:10.1016/j.foodchem.2022.134543] [https://hdl.handle.net/10807/232216]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/232216
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