Por favor, use este identificador para citar o enlazar a este item: http://hdl.handle.net/10261/229187
COMPARTIR / EXPORTAR:
logo share SHARE BASE
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE

Invitar a revisión por pares abierta
Título

Large-scale evaluation of shotgun triacylglycerol profiling for the fast detection of olive oil adulteration

AutorQuintanilla-Casas, Beatriz; Strocchi, Giulia; Bustamante, Julen; Torres-Cobos, Berta; Guardiola, Francesc; Moreda, Wenceslao CSIC ORCID ; Martínez-Rivas, José Manuel CSIC ORCID ; Valli, Enrico; Bendini, Alessandra; Gallina Toschi, Tullia; Tres, Alba; Vichi, Stefania CSIC ORCID
Palabras claveOlive oil
Adulteration
High resolution mass spectrometry
Shotgun lipidomics
Triacylglycerols
Screening
Fecha de publicaciónmay-2021
EditorElsevier
CitaciónFood Control 123: 107851 (2021)
ResumenFast and effective analytical screening tools providing new suitable authenticity markers and applicable to a large number of samples are required to efficiently control the global olive oil (OO) production, and allow the rapid detection of low levels of adulterants even with fatty acid composition similar to OO. The present study aims to develop authentication models for the comprehensive detection of illegal blends of OO with adulterants including different types of high linoleic (HL) and high oleic (HO) vegetable oils at low concentrations (2–10%) based on shotgun triacylglycerol (TAG) profile obtained by Flow Injection Analysis-Heated Electrospray Ionisation-High Resolution Mass Spectrometry (FIA-HESI-HRMS) at a large-scale experimental design. The sample set covers a large natural variability of both OO and adulterants, resulting in more than one thousand samples analysed. A combined PLS-DA binary modelling based on shotgun TAG profiling proved to be a fit for purpose screening tool in terms of efficiency and applicability. The external validation resulted in the correct classification of the 86.8% of the adulterated samples (diagnostic sensitivity = 0.87), and the 81.1% of the genuine samples (diagnostic specificity = 0.81), with an 85.1% overall correct classification (efficiency = 0.85).
Descripción3 Figuras.-- 4 Tablas
Versión del editorhttp://dx.doi.org/10.1016/j.foodcont.2020.107851
URIhttp://hdl.handle.net/10261/229187
ISSN0956-7135
Aparece en las colecciones: (IG) Artículos

Ficheros en este ítem:
Mostrar el registro completo

CORE Recommender

Page view(s)

120
checked on 19-abr-2024

Download(s)

161
checked on 19-abr-2024

Google ScholarTM

Check


Este item está licenciado bajo una Licencia Creative Commons Creative Commons