## 3 Results and Discussion
Against this background, the present study made a comparison among the efficiency of three ML algorithms (i.e., ANN, SVM and KNN) for the geographical differentiation of honey samples originated from Romania and other EU countries. In order to enhance the discrimination power of the developed models, a data dimensionality reduction step was performed before the construction of the AI-based models. For this purpose, an unsupervised statistical method (PCA) and a supervised one (based on PLS) for the feature selection were applied and further compared. The most accurate model for the differentiation of Romanian samples from those produced in other EU countries proved to be the one obtained through the application of the ANN algorithm, when a feature selection step based on PLS was performed prior to model construction. In this case, an accuracy of 95% was obtained, the Romanian samples being correctly predicted with a percentage of 99%, while the ones produced outside Romania were rightly attributed with a precision of 73% to the foreign group. The above-described approach (i.e., the development of an ANN model on the data associated with the most significant variables, determined through the PLS-based technique) was the most effective one also for the discrimination of Transylvanian samples from the other Romanian ones. In this case, a model with an accuracy of 85% was built. It was possible to correctly identify the samples produced in Transylvania with a percentage of 93%, while the honey originating from other Romanian regions was correctly identified in 72% of the cases. The least efficient prediction models corresponded to those built on the KNN algorithm for both types of classifications, either at the intercountry or intracountry level. Moreover, by using this ML algorithm, the weakest differentiation of the honey samples as compared to ANN and SVM was obtained, independently of the type of input data (i.e., the entire set of isotope and elemental content or a reduced variable set). Based on these results, the association between isotopic and elemental content with AI demonstrated a high effectiveness for the development of robust models for the geographical recognition of honey. Reducing the input data to the features having the highest discrimination power permitted the development of more accurate prediction models, for both types of classification types and regardless of the ML algorithm employed. In order to better validate our proposed approach, our future work aims to employ a higher number of honey samples in the models' development, especially for the foreign group. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app122110894/s1](https://www.mdpi.com/article/10.3390/app122110894/s1)]([https://www.mdpi.com/article/10.3390/app122110894/s1](https://www.mdpi.com/article/10.3390/app122110894/s1)). Conceptualization, A.R.H. and D.A.M. ; methodology, G.C., R.P., A.D. and B.S. ; software, A.R.H. ; validation, A.R.H. and D.A.M. ; formal analysis, G.C., R.P. and A.D.; investigation, G.C., R.P., A.D. and B.S. ; resources, G.C., R.P. and A.D.; data curation, A.R.H. ; writing--original draft preparation, A.R.H. and D.A.M. ; writing--review and editing, A.R.H., G.C., R.P., A.D., B.S. and D.A.M. ; visualization, A.R.H. ; supervision, D.A.M. ; project administration, D.A.M. ; funding acquisition, D.A.M. All authors have read and agreed to the published version of the manuscript. This work was supported by a grant of the Romanian Ministry of Education and Research, CNCS-UEFISCDI, project number PN-III-P4-ID-PCE-2020-0644 (Contract no. 7 PCE/2021), within PNCDI III, and to the Ministry of Research, Innovation and Digitalisation through Programme 1-Development of the National Research and Development System, Subprogramme 1.2-Institutional Performance-Funding Projects for Excellence in RDI, Contract No. 37 FE/30.12.2021. A.R.H.