Non-Targeted Authentication Approach for Extra Virgin Olive Oil

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 ( = 151) were grouped as EVOO, virgin olive oil (VOO)/olive oil (OO), and EVOO a...

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Bibliographic Details
Published in:Foods 2020-02, Vol.9 (2), p.221
Main Authors: Aykas, Didem Peren, Karaman, Ayse Demet, Keser, Burcu, Rodriguez-Saona, Luis
Format: Article
Language:English
Subjects:
authenticity
Chromatography
Classification
Data collection
Discriminant analysis
extra virgin olive oil
Fatty acids
Food
Food science
Food supply
ft-ir
Grocery stores
Infrared analysis
Infrared spectroscopy
Laboratories
Methods
Oils & fats
Olive oil
Pattern analysis
Pattern recognition
Peroxide
Quality control
Quality standards
raman
Regression analysis
Regression models
Sensitivity analysis
Sensors
Spectral sensitivity
Spectroscopy
Vegetable oils
Vegetables
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Summary: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 ( = 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.
ISSN:2304-8158
2304-8158
DOI:10.3390/foods9020221