## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline
**Input Space (Number of Attributes)** & **Machine Learning Classifier** & **True Positive Rate (Cross-Validation)** & **Accuracy** \\  & **Classifier** & **Transylvania (60)** & **Other (36)** & **(Cross-Validation)** \\ \hline \multirow{3}{*}{Entire set of isotopic and elemental determinations (54)} & ANN & 0.85 & 0.55 & 0.73 \\  & SVM & 0.98 & 0.36 & 0.75 \\  & KNN & 0.83 & 0.58 & 0.73 \\ \hline \multirow{3}{*}{PCA scores (10)} & ANN & 0.80 & 0.69 & 0.76 \\  & SVM & 0.90 & 0.44 & 0.72 \\  & KNN & 0.95 & 0.30 & 0.70 \\ \hline \multirow{3}{*}{Features selected based on PLS (19)} & ANN & 0.93 & 0.72 & 0.85 \\  & SVM & 0.91 & 0.72 & 0.84 \\ \cline{1-1}  & KNN & 0.93 & 0.44 & 0.75 \\ \hline \end{tabular}
\end{table} 
Table 1: Performance of the developed artificial neural networks (ANN), support vector machines (SVM) and k-nearest neighbors (KNN) for classifying the Romanian honey samples with respect to the geographical origin (Transylvania or other regions). that were not produced in Transylvania. In contrast to this, the KNN model proved to be less efficient, reaching a maximum accuracy score of 75%. The second category of prediction models aimed to classify the honey samples according to their country of origin, namely to differentiate the Romanian samples from the ones produced in other foreign countries. In the case when the input data for the ML algorithms included the entire set of isotope and elemental determinations, the most efficient classifier was represented by the SVM model, which was able to correctly classify 125 out of 136 samples with respect to the production country (Table 2). Therefore, 97% of the Romanian samples and 57% of the other ones were accurately assigned to the geographical source after the 10-fold cross-validation procedure. The performance of the ANN model was similar to the classification results obtained through the application of SVM, as it conducted to an overall accuracy score of 91% (i.e., 124 out of 136 samples were correctly predicted). On the other hand, the KNN model, though characterized by an 88% accuracy score, was able to assign only 5 out of 19 non-Romanian samples to the right geographical class. By developing the same type of classifiers based on the scores corresponding to the first 10 principal components, which explained more than 70% of the total variance, no improvements were observed in the performance of the ML-based models. All three algorithms conducted to similar results, namely accuracy scores of 89%, 88% and 87%, were obtained by applying KNN, SVM and ANN, respectively (Table 2). The supervised method for identifying the attributes that have a higher discrimination power conducted to the development of classification models based on the following markers: \(\delta^{2}\)H, \(\delta^{18}\)O, B, V, Mn, Rb, Ba, Ce and K (Table S2). As opposed to the dimensionality reduction step through PCA, this approach succeeded in enhancing the differentiation ability of all ML classifiers (Table 2). In this regard, the most efficient model (i.e., ANN) classified the honey samples with an accuracy of 95%. An aspect worth mentioning is the fact that, as compared to initial classification model that used the entire set of isotope and elemental measurements, the ANN model constructed on the input space corresponding to the most significant features proved to have a much higher ability in identifying the right geographical class for the samples produced in countries other than Romania (i.e., reaching a 73% true positive rate for this group). The other two ML models, namely SVM and KNN, correctly classified the sample set in 93% and 92% of the cases, respectively. The values associated with the configuration parameters of the developed ANN, SVM and KNN models are presented in Tables S3-S5, respectively. the product price is directly influenced by the labeled geographical appurtenance.