Detection and Identification of Extra Virgin Olive Oil Adulteration by GC-MS Combined with Chemometrics

In this study, an analytical method for the detection and identification of extra virgin olive oil adulteration with four types of oils (corn, peanut, rapeseed, and sunflower oils) was proposed. The variables under evaluation included 22 fatty acids and 6 other significant parameters (the ratio of l...

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Bibliographic Details
Published in:Journal of agricultural and food chemistry 2013-04, Vol.61 (15), p.3693-3702
Main Authors: Yang, Yang, Ferro, Miguel Duarte, Cavaco, Isabel, Liang, Yizeng
Format: Article
Language:English
Subjects:
adulterated products
Biological and medical sciences
Canola Oil
chemometrics
corn
Corn Oil - chemistry
detection limit
erucic acid
extra-virgin olive oil
Fat industries
Fatty Acids - analysis
Fatty Acids, Monounsaturated
Food Contamination
Food industries
Food Inspection - methods
Food Labeling
Fruit - chemistry
Fundamental and applied biological sciences. Psychology
General aspects
Informatics - methods
linoleic acid
linolenic acid
Methods of analysis, processing and quality control, regulation, standards
multivariate analysis
Olea - chemistry
Olive Oil
Peanut Oil
peanuts
Plant Oils - chemistry
prediction
rapeseed
saturated fatty acids
Sunflower Oil
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Summary:In this study, an analytical method for the detection and identification of extra virgin olive oil adulteration with four types of oils (corn, peanut, rapeseed, and sunflower oils) was proposed. The variables under evaluation included 22 fatty acids and 6 other significant parameters (the ratio of linoleic/linolenic acid, oleic/linoleic acid, total saturated fatty acids (SFAs), polyunsaturated fatty acids (PUFAs), monounsaturated fatty acids (MUFAs), MUFAs/PUFAs). Univariate analyses followed by multivariate analyses were applied to the adulteration investigation. As a result, the univariate analyses demonstrated that higher contents of eicosanoic acid, docosanoic acid, tetracosanoic acid, and SFAs were the peculiarities of peanut adulteration and higher levels of linolenic acid, 11-eicosenoic acid, erucic acid, and nervonic acid the characteristics of rapeseed adulteration. Then, PLS-LDA made the detection of adulteration effective with a 1% detection limit and 90% prediction ability; a Monte Carlo tree identified the type of adulteration with 85% prediction ability.
ISSN:0021-8561
1520-5118
DOI:10.1021/jf4000538