Tracing the geographical origin of Argentinean lemon juices based on trace element profiles using advanced chemometric techniques

Abstract

This study examines the application of chemometric techniques associated with trace element concentrations for origin evaluation of lemon juice samples. Seventy-four lemon juice samples from three different provinces of Argentina were evaluated according to their microelement contents to identify differences in patterns of elements in the three provinces. Inductively coupled plasma mass spectrometry (ICP-MS) was used for the determination of twenty-five elements (Ag, Al, As, Ba, Bi, Co, Cr, Cu, Fe, Ga, In, La, Li, Mn, Mo, Ni, Rb, Sb, Sc, Se, Sn, Sr, Tl, V, and Zn). Once the analytical data were collected, supervised pattern recognition techniques were applied to construct classification/discrimination rules to predict the origin of samples on the basis of their profiles of trace elements. Namely, linear discriminant analysis (LDA), partial least square discriminant analysis (PLS-DA), k-nearest neighbors (k-NN), random forest (RF), and support vector machine with radial basis function Kernel (SVM). The results indicated that it was feasible to attribute unknown lemon juice samples to its geographical origin. SVM had better performance compared to RF, k-NN, LDA and PLS-DA, listed in descending order. Eventually, this study verifies that trace element pattern is a powerful geographical indicator when identifying the origin of lemon juice samples by analyzing trace element data with the help of SVM technique. This level of accuracy provides an interesting foundation to propose the combination of trace element contents with SVM technique as a valuable tool to evaluate the geographical origin of lemon juice samples produced in Argentina.