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Non-Targeted Authentication Approach for Extra Virgin Olive Oil.
Aykas, Didem Peren; Karaman, Ayse Demet; Keser, Burcu; Rodriguez-Saona, Luis.
Afiliación
  • Aykas DP; Department of Food Science and Technology, The Ohio State University, 100 Parker Food Science and Technology Building, 2015 Fyffe Road, Columbus, OH 43210, USA.
  • Karaman AD; Department of Food Engineering, Faculty of Engineering, Adnan Menderes University, Aydin 09100, Turkey.
  • Keser B; Department of Dairy Technology, Faculty of Agricultural Engineering, Adnan Menderes University, Aydin 09100, Turkey.
  • Rodriguez-Saona L; Kocarli Vocational School, Adnan Menderes University, Aydin 09100, Turkey.
Foods ; 9(2)2020 Feb 20.
Article en En | MEDLINE | ID: mdl-32093145
ABSTRACT
The aim of this study is to develop a non-targeted approach for the authentication of extra virgin olive oil (EVOO) using vibrational spectroscopy signatures combined with pattern recognition analysis. Olive oil samples (n = 151) were grouped as EVOO, virgin olive oil (VOO)/olive oil (OO), and EVOO adulterated with vegetable oils. Spectral data was collected using a compact benchtop Raman (1064 nm) and a portable ATR-IR (5-reflections) units. Oils were characterized by their fatty acid profile, free fatty acids (FFA), peroxide value (PV), pyropheophytins (PPP), and total polar compounds (TPC) through the official methods. The soft independent model of class analogy analysis using ATR-IR spectra showed excellent sensitivity (100%) and specificity (89%) for detection of EVOO. Both techniques identified EVOO adulteration with vegetable oils, but Raman showed limited resolution detecting VOO/OO tampering. Partial least squares regression models showed excellent correlation (Rval ≥ 0.92) with reference tests and standard errors of prediction that would allow for quality control applications.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Foods Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Foods Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos