## 3 Results and Discussion
The performance of the PLS-DA models developed for differentiating the meat samples with respect to their geographical origin is displayed in Table 2. Sensitivity refers to the ratio between the number of samples correctly identified as being part of a class and the total number of samples in that class, whereas specificity is the number of samples correctly predicted not to be part of a class divided by the actual number of samples not belonging to the class [32]. RMSEC (root mean square error in calibration) is a measure of how well the model fits the experimental concentrations, whereas RMSECV (root mean square error of cross-validation) is a measure of how well the model will predict the class of the validation samples [33]. **The chicken meat samples** from Arad, Calarasi, and Cluj were _correctly classified_ in percentages of 91.6, 100, and 93.9%, respectively, leading to reliable results with an _accuracy_ rate of 93.8%. Therefore, the number of correctly differentiated samples with respect to their geographical origin was 46 out of 49 samples (11-Arad, 4-Calarasi, and 31-Cluj). One meat sample from Arad was wrongly assigned to Cluj, while two samples from Cluj were mistakenly predicted as samples from Arad and Calarasi, respectively. A percentage of 60.8, 91.6, 100, and 70.0% of **pork meat samples** from Cluj, Iasi, Maramures, and Salaj were _correctly classified_ by the PLS-DA statistical method. Nine out of twenty-three meat samples from Cluj were incorrectly assigned (one sample to Iasi, three samples to Salaj, and five samples to Alba). One sample from Iasi was assigned to the Maramures group, and six samples from Salaj were predicted to belong to the Cluj group. The identification of the correct geographical region in the case of samples from Alba was unsatisfactory and might be explained by the fact that the pork meat samples came from neighboring geographical regions with very similar geographical conditions, \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Meat Models** & **Origin** & **RMSEC \({}^{*}\)** & **RMSECV \({}^{**}\)** & **Sensitivity** & **Specificity** & **Sensitivity** & **Specificity** & **CV** \\ \hline \multirow{3}{*}{**chicken**} & Arad & 0.26 & 0.35 & 1.00 & 0.94 & 0.91 & 0.91 \\  & Calarasi & 0.19 & 0.29 & 1.00 & 0.97 & 1.00 & 0.95 \\  & Cluj & 0.25 & 0.30 & 0.97 & 1.00 & 0.97 & 1.00 \\ \hline \multirow{3}{*}{**pork**} & Alba & 0.22 & 0.23 & 1.00 & 0.64 & 0.75 & 0.65 \\  & Cluj & 0.35 & 0.38 & 0.95 & 0.77 & 0.91 & 0.75 \\ \cline{1-1}  & Iasi & 0.20 & 0.22 & 1.00 & 0.94 & 0.91 & 0.93 \\ \cline{1-1}  & Maramures & 0.10 & 0.12 & 1.00 & 1.00 & 1.00 & 0.98 \\ \cline{1-1}  & Salaj & 0.32 & 0.36 & 0.75 & 0.90 & 0.70 & 0.86 \\ \hline \hline \end{tabular}  
* RMSEC—Root mean square error in calibration, ** RMSECV—Root mean square error in cross-validation.  
\end{table} 
Table 2: Results of PLS-DA models applied for chicken and pork meat samples; dataset, for both the calibration set and cross-validation test. like the Alba/Cluj areas. Therefore, the PLS-DA model was characterized by an _accuracy score_ of 71.8% when classifying **pork meat samples** according to their geographical origin. Figure 4 shows the graphical representation of the PLS-DA results of chicken and pork meat samples based on the first three latent variables (LVs), which captured 58.67% of the variance for chicken meat and 39.86% for pork meat, respectively. Among the best predictors obtained for the geographical origin differentiation of meat samples, 11 (B, Na, K, V, As, Rb, Nb, Sn, \(\delta^{13}\)C, \(\delta^{2}\)H, and \(\delta^{18}\)O) are common for both groups (chicken and pork). Meat contains very low concentrations of boron as compared to boron-rich foods such as prunes (20-30 \(\mu\)g/g), raising (22 \(\mu\)g/g), or peanuts (17 \(\mu\)g/g) [34]. Boron improves magnesium absorption and calcium retention. However, boron is an essential trace element for plants. The quantity of bioavailable boron in soil can influence the boron concentration in plants, and then the animals will consume the plants [35].