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Adulteration detection of argan oil by inductively coupled plasma optical emission spectrometry

Gonzálvez, A., Armenta, S., de la Guardia, M.
Food chemistry 2010 v.121 no.3 pp. 878-886
Argania spinosa, seed oils, adulterated products, multivariate analysis, detection, trace elements, mineral content, sunflower oil, olive oil, food composition, foods, soybean oil, accuracy, spectroscopy, analytical methods
The singularity of the trace element profile of argan oil has been demonstrated by means of inductively coupled plasma optical emission measurement in combination with different chemometric approaches. The ability of multivariate analysis methods; such as hierarchical cluster analysis (HCA), principal component analysis (PCA), classification trees using Chi-squared Automatic Interaction Detector (CHAID) and discriminant analysis (DA) to achieve edible oils classification based on its type or variety from their elemental content have been investigated. The calculations were performed using 16 variables (contents of Na, Mg, Al, K, Ca, Ti, Fe, Co, Ni, Cu, Zn, Cd, Pr, Sm, Er and Bi at μgg⁻¹ level determined by ICP-OES). HCA is able to differentiate sunflower oil samples from the rest, however the discrimination of argan oil from olive, seeds and soya oils based on their different trace element composition is hard to achieve. The PCA analysis shows three different classes in the multidimensional space (PC1-3) representing sunflower, argan and a third group comprising olive, seeds and soya oils. CHAID method allows separating the entire vegetable oil dataset, providing a correct re-substitution rate of 94.12% for argan oil using only the concentration of K. DA performed using the same variables, provides also an acceptable average accuracy results of 93.65%, by the re-substitution method. DA has been successfully applied to the analysis adulterated argan oil by addition of cheaper vegetable oils.