## 4 Conclusions  
In this work, the isotopic and elemental profiles of 40 berry samples--from different botanical origins (raspberry, seaberry, blackberry, cranberry, and blueberry) and from two growing regimes (wild and cultivated)--were assessed. The composition of berries depends on their species, growing conditions (cultivated or wild), and type of soil, with observable variability in both the stable isotopic ratios and elemental content. In order to discriminate berries according to the botanical origin and growing system, PLS-DA models were built. Moreover, a variable selection approach was applied to determine the variables that have the highest discrimination power with respect to each classification criteria. Thus, \(\delta^{13}\)C, \(\delta^{2}\)H, \(\delta^{18}\)O, Li, Na, Mg, P, Ca, V, Mn, Co, Ni, Zn, As, Rb, Sr, Ba, and Eu were identified as the most relevant attributes for the berry species, while \(\delta^{13}\)C, \(\delta^{18}\)O, Li, Na, Ca, Cr, Mn, Ni, Rb, and Ba corresponded to the growing regime markers. By applying PLS-DA on the most significant features identified, correct classification rates of 94.4% (for cultivated berries) and 77.2% (for wild berries) were obtained in the cross-validation procedure, leading to a model accuracy of 85%. Similarly, an 85% accuracy was achieved through the PLS-DA model built for the varietal discrimination of the samples. Additionally, the developed PLS-DA model for the discrimination of wild blueberries from cultivated ones showed excellent levels of sensitivity (100%), specificity (100%), and accuracy (100%).  
This preliminary study indicated the fact that the corroboration of analytical methods (IRMS and ICP-MS), followed by chemometric treatment using PLS-DA, as a statistical supervised method, could be utilized as an authenticity testing tool for berries according to growing system. For future studies, the sample's data set will be enlarged to provide more significant results. In addition, different statistical models will be developed to authenticate the geographical origin of different types of berries based on isotopic and elemental fingerprints.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13052980/s1](https://www.mdpi.com/article/10.3390/app13052980/s1)]([https://www.mdpi.com/article/10.3390/app13052980/s1](https://www.mdpi.com/article/10.3390/app13052980/s1)).  
Conceptualization, A.D. and G.C.; methodology, A.D., G.C. and R.P.; 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. and A.D.; resources, R.P., A.D., F.-D.C. and C.M.M.; data curation, A.R.H. and A.D.; writing--original draft preparation, A.D., G.C. and D.A.M.; writing--review and editing, A.D., G.C. and D.A.M.; visualization, A.D. and G.C.; supervision, D.A.M.; project administration, D.A.M.; funding acquisition, D.A.M. and C.M.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the MCID through the \"Nucleu\" Program within the National Plan for Research, Development, and Innovation 2022-2027, contract no. 27N/2023, PN 23 24 03 01, and a grant from the Ministry of Research, Innovation and Digitalization, CCCDI-UEFISCDI, project number PN-III-P2-2.1-PED-2019-3004 (contract no. 359 PED/2020), within PNCDI III.  
Not applicable.  
**Data Availability Statement:** Samples are not available from the authors.  
The authors declare no conflict of interest.