## 3 Results and Discussion
For investigated _pork meat_, the concentration levels of the elements (as mean values) follow a decreasing trend, namely: K > Na > Mg > Ca (macroelements), Zn > Fe > Rb > Cr > Cu > Ni > B > Ba > Se > Mn > Mo > Sr > Li (microelements), Pd > Co > V > La > Nb > In (trace elements), and Pb > Sn > As > Cd > Sb (potentially toxic elements), respectively. Some researchers [23; 24] discovered a similar trend in microelement concentrations in Korean and Serbian pork meat. The same decreasing order was obtained by [PERSON] et al. (2021) [23] for microelement levels in animal welfare and conventional pork samples. The concentration ranges (mg/kg) of the most abundant macro minerals were K (1093.1-4161.3), followed by Na (303.3-5681.1), Mg (123.1-405.9), and Ca (19.2-405.0). There have been published studies [21; 22; 23; 25; 26] on the levels of macro- and micro-elements (in mg/kg) in pork meat from other countries, such as: Korea (3600-4430 for K, 360-410 for Na, 210-270 for Mg, 40-60 for Ca, 0.64-0.85 for Cu, 5.37-13.26 for Fe, 0.16-0.28 for Mn, 0.21-0.43 for Se, and 12.24-14.79 for Zn), Croatia (2.6-4440 for K, 17-346 for Mg and 0.36-65 for Ca, 0.38-1.1 for Cu, 0.66-56 for Fe, 0.03-0.81 for Mn, 0.03-0.26 for Se, and 11-90 for Zn), China (15046 and 14,369 for K, 1343 and 1426 for Na, 942 and 958 for Mg, 127 and 145 for Ca, 1.80 and 1.49 for Cu, 22.6 and 18.9 for Zn, 0.695 and 0.473 for Mn, 0.151 and 0.291 for Se, and 50.8 and 49.1 for Zn in organic and conventional rearing system, respectively), Brazil (355-1266 for Mg, 1.8-7.2 for Cu, 3.9-69 for Fe, 0.081-0.563 for Mn, 0.280-1.150 for Se, 26-176 for Zn, and 0.025-0.085 for Mo) and South Africa (211.2-263.2 for Mg, 2868-3684 for K, 712-830 for Na, 285-397 for Ca, 1.1-1.7 for Fe, and 2.0-2.3 for Zn). As reported in the literature, the elemental content of the meat depends on both the animal species [27; 28] and the geographical origin [12; 29], animal rearing system, or animal feeding [25; 30; 31], respectively. In order _to find the most significant parameters_ that differentiate the meat according to _the geographical origin_ and _the growing system_, a model-based feature selection based on Partial Least Squares (PLS) was applied in the present study. The identification of the most relevant parameters is crucial in constructing efficient discrimination models. In this study, the feature selection procedure was performed before the final model construction in order to identify those variables that have the highest discrimination potential for a specific classification. Thus, the classification models constructed through PLS-DA were based on the parameters identified through the selection step. _Classification of chicken and pork meat according to their geographical origin_  
The dataset consisted of 120 samples (49 chicken meat and 71 pork meat) separated by regions (Cluj, \(n=33\); Arad, \(n=12\); and Calarasi, \(n=4\), for chicken samples; Maramures, \(n=12\); Salaj, \(n=20\); Cluj, \(n=23\); Alba, \(n=4\); and Iasi, \(n=12\), for pork samples) and 31 variables (\(\delta^{13}\)C, \(\delta^{2}\)H, \(\delta^{18}\)O, Li, B, Na, Mg, K, Ca, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Rb, Sr, Nb, La, Mo, Pd, Cd, In, Sn, Sb, Ba, and Pb). By applying the feature _selection procedure_, the relevant variables for the discrimination model of the geographical origin of meat samples were identified. Thus, the _main predictors_ for the geographical origin differentiation of the samples were: B, Na, K, V, As, Se, Rb, Nb, Ag, Cd, Sn, Ir, \(\delta^{13}\)C, \(\delta^{2}\)H, and \(\delta^{18}\)O (for chicken meat) and B, Na, Mg, K, Ca, V, Cr, Fe, Ni, Cu, Zn, As, Rb, Sr, Nb, Mo, Sn, Sb, Ba, Pb, \(\delta^{13}\)C, \(\delta^{2}\)H, and \(\delta^{18}\)O (for pork meat).