Discrimination of pistachio cultivars based on multi-elemental fingerprinting by pattern recognition methods

The potential of multi-elemental fingerprinting based on inductively coupled plasma optical emission measurements (ICP OES) was examined to classify pistachio cultivars. Five pistachio cultivar samples were collected during the harvesting period 2017–2018 from Kerman in Iran. The ability of multivariate data analysis approaches, such as principal component analysis (PCA) and principal component analysis-linear discriminant analysis (PCA-LDA) have been investigated in order to achieve discrimination of different cultivars. Eighteen variables i.e. the contents of Al, B, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Na, Ni, Pb, Sr and Zn at mg g−1 level, determined by ICP OES were used. The results showed that the five pistachio cultivars can be identified based on the multi-elemental fingerprints. The loading plot of PCA illustrated that Pb, Cr, Co, Cd, and Ba have the lowest contributions in discrimination of the different pistachio cultivars. Almost all samples were correctly classified by the PCA-LDA model using cross validation (99.0%). The mean sensitivity and specificity were 98.0% and 99.6%, respectively, indicating the satisfactory performance of the model. The results demonstrate multi-elemental fingerprinting combined with multivariate data analysis methods can be employed as an effective and feasible method for classification of Iranian pistachio based on their cultivars.