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

Abstract

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.

Identifying and Quantifying Adulterants in Extra Virgin Olive Oil of the Picual Varietal by Absorption Spectroscopy and Nonlinear Modeling

“…In comparison to other techniques of adulterant determination and authentication of EVOO, such as GC-MS used by Yang et al, we can observe that their identification rate is of 88.5%. 38 On the other hand, HPLC used by De la MataEspinosa et al gave a root-mean-square error of prediction of 10% for the adulterant determination, 21 whereas that same error calculated with the results here obtained is of 9.5%. This is very interesting considering that our technique is much cheaper and less time-consuming than GC-MS or HPLC.…”

Barcode DNA length polymorphisms vs fatty acid profiling for adulteration detection in olive oil

“…GC analysis of the fatty acid composition also proved efficient in identifying adulteration in the samples. Erucic acid (C22:1) is normally present in undetectable amounts in olive oil and indicated as a marker of rapeseed oil adulteration (Yang et al, 2013). In this study, with values that ranged between 1.2 and 2.4%, erucic acid was the only fatty acid that allowed adulteration detection down to a limit of 5%, enabling the detection of rapeseed oil in all of the tested samples ( Table 2).…”

“…The most commonly utilized approach is analysis of fatty acid composition by gas chromatography (GC) (Aparicio & Aparicio-Ruiz, 2000;Ulberth & Buchgraber, 2000;Yang, Ferro, Cavaco, & Liang, 2013). Analysis of the intact triacylglycerol composition by reversed-phase high-performance liquid chromatography (HPLC) is a complementary/alternative approach to fatty acid profiling by GC (Aparicio & Aparicio-Ruiz, 2000;Christopoulou, Lazaraki, Komaitis, & Kaselimis, 2004;Jabeur et al, 2014;Ulberth & Buchgraber, 2000).…”

“…Spectroscopic methods that require less complicated sample preparation steps than labor intensive chromatography are also gaining popularity for determination of edible oil authenticity (Costa et al, 2012). However, spectroscopic methods only give a rough idea of sample composition without any information about potential adulterants (Yang et al, 2013). All of the above mentioned chromatographic and spectroscopic analytical approaches rely on the assumption that the chemical composition of a blend will significantly deviate from that of olive oil due to a dramatic difference in the quantity of a given analyte between olive oil and the adulterant oil species.…”

Partial Least Squares Discriminant Analysis Model Based on Variable Selection Applied to Identify the Adulterated Olive Oil

“…For above reasons, the adulteration of EVOO has been a serious problem. Olive oils of lower quality or other cheap vegetable oils (such as sunflower, corn, soybean, and peanut oil) are occasionally doped into EVOO by some unscrupulous merchants (Torrecilla et al 2010;Mildner-Szkudlarz and Jelen 2008;Yang et al 2013). Therefore, there is no doubt that some detecting techniques should be employed to protect consumers from fraud.…”