## 3 Results and Discussion
For the past 50 years, B has been used on B-deficient soils to improve yields of crops of grains, vegetables, and fruits [36]. Boron was also recorded as a discrimination marker for geographical origin in our previous study related to Transylvanian fruit spirits [9]. It was not surprising to find Na among the primary parameters in geographical origin differentiation. Sodium was also found as a discriminator marker for Transylvanian pork meat [12] and vegetables [37], due to the fact that this region is recognized as having a higher Na content in the soil. Soil characteristics differ among different regions. Thus, the concentration and availability of minerals in soil are reflected in plants, and then in the animals that consume the plants. Arsenic was identified as a predictor for the geographical origin assignment of Romanian honey [4], potatoes grown in distinct regions of Romania [38], and the authentication of Transylvanian fruit spirits [10]. It is a well-known fact that \(\delta^{2}\)H and \(\delta^{18}\)O are tracers for geographical origin because the chicken and pork body water takes its rise from drinking water, which in turn depends on latitude, altitude, and distance from the sea [11], reflecting a region-specific distribution [39]. _Discrimination of the meat samples according to the animal's growing system_  
A similar strategy to the one followed for the geographical origin authentication of meat samples was adopted: selecting the most relevant parameters that contribute to the discrimination of the animal breeding system (animals coming from yard rearing systems versus animals coming from conventional farms) and subsequently using them as input data for the PLS-DA models. The sample set included 49 chicken meat samples (33 from yard rearing systems and 16 from conventional farms) and 71 pork meat samples (47 from yard rearing systems and 24 from conventional farms). The final PLS-DA models (Figure 5) were built based on the variables that have the highest discrimination potential, as identified in the feature selection procedure, namely: B, K, V, Cr, Mn, Fe, Cu, Zn, Se, Rb, Nb, Sn, \(\delta^{13}\)C, and \(\delta^{2}\)H (for chicken meat) and Se, Rb, Nb, Sb, Ba, Pb, and \(\delta^{13}\)C (for pork meat). The developed PLS-DA models showed excellent levels of sensitivity and specificity (Table 3). In accordance with the cross-validation results, the PLS-DA models were able to differentiate the meat samples with respect to the animal breeding system with an accuracy of 100% (for pork meat), and 97.9% (for chicken meat), respectively. Figure 4: PLS-DA score plot obtained for the classification of meat samples according to their geographical origin: (**A**) chicken and (**B**) pork. Figure 5: PLS-DA score plot obtained for meat sample discrimination based on animal growing system: (**A**) chicken and (**B**) pork. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Animal Coming from Yard Rearing**} & \multicolumn{2}{c}{**Animal Coming from**} \\
**Parameter** & \multicolumn{2}{c}{**System**} & \multicolumn{2}{c}{**Conventional System**} \\ \cline{2-5}  & **Chicken** & **Pork** & **Chicken** & **Pork** \\ \hline
**RMSEC \({}^{*}\)** & 0.23 & 0.24 & 0.23 & 0.24 \\
**RMSECV \({}^{**}\)** & 0.26 & 0.24 & 0.26 & 0.24 \\
**Sensitivity (Cal)** & 0.97 & 1.00 & 1.00 & 1.00 \\
**Sensitivity (CV)** & 0.97 & 1.00 & 1.00 & 1.00 \\
**Specificity (Cal)** & 1.00 & 1.00 & 0.97 & 1.00 \\
**Sensitivity (CV)** & 1.00 & 1.00 & 0.97 & 1.00 \\ \hline \hline \end{tabular}  
* RMSEC—Root mean square error in calibration, ** RMSECV—Root mean square error in cross-validation.  
\end{table} 
Table 3: Classification results obtained by applying PLS-DA for the differentiation of meat samples according to the animal breeding system.