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Artificial Intelligence strategies based on GC×GC-MS/FID patterns capture extra-virgin olive oil aroma blueprint and unique identity

Year: 2022

Type: conference-paper

Abstract: The contribution illustrates the potentials of GC×GC-MS/FID platforms in the context of Artificial Intelligence Smelling and computer vision tools for extra-virgin olive oils characterization and identitation. By accurate quantification of key-aromas and odorants strongly correlated to sensory defects, samples’ aroma blueprint is captured and used to discriminate oils based on peculiar hedonic features.

Source: Institutional Research Information System University of Turin (University of Turin)

Authors: Chiara Cordero, Simone Squara, Federico Stilo, Andrea Caratti, Erica Liberto, Carlo Bicchi, Stephen E. Reichenbach, Luis Cuadros-Rodriguez, and Humberto Bizzo

Institution: University of Turin

Language: English

Topic: Edible Oils Quality and Analysis

Subfield: Organic Chemistry

Field: Chemistry

Domain: Physical Sciences

Open Access status: green

Funders: Regione Puglia, Bayerische Staatsministerium für Wirtschaft, Landesentwicklung und Energie

OpenAlex ID: W7217280746

FWCI:

Cites: 29

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Related to: 10