## 5 Conclusions  
This study proposes the development of new honey botanical recognition models based on the corroboration between ATR-FTIR spectroscopy and either supervised chemometric method or artificial intelligence. In this way, it was possible to perform a comparison between the efficiency of the recognition models built using advanced statistical methods and those performed based on artificial intelligence. For the models based on the supervised method PLS-DA, the dimensionality reduction step led in some cases to an improvement of the classification rate. The comparison of the models' performances developed on PLS-DA with those based on SVM clearly resulted in a better classification rate of three out of five developed models. Thus, for fir differentiation model using SVM an increased performance of 15% (from 75% using PLS-DA to 90%), for coriander a 10% increase, for raspberry an increase of 78% to 92%, and for sunflower an increase from 87% to 93% was observed. In contrast, in the other discrimination classes chestnut and honeydew the performances remained the same. Overall, the developed differentiation models can be considered to be effective for the assumed purpose taking into account that we tried to emphasize differences between very similar honey varieties (i.e., fir and honeydew samples). By including into investigations of the polyfloral honey types, it was expected to get a lower classification of the developed models by considering that in these honey varieties, a mixture of several floral contributions in distinct percentages can be found.  
Conceptualization, D.A.M.; Formal analysis, A.R.H. and C.B.-G.; Funding acquisition, D.A.M.; Investigation, M.D. and A.R.H.; Methodology, C.B.-G.; Project administration, D.A.M.; Resources, B.S.; Software, A.R.H.; Supervision, D.A.M.; Visualization, B.S.; Writing--original draft, M.D., A.R.H., D.A.M., C.B.-G. and B.S. 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.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Differentiated** & **Input Data** & **Kernel** & **C** & **Gamma** & **Degree** \\ \hline \multirow{2}{*}{chestnut} & all features & radial basis function & \(2^{3}\) & \(2^{-13}\) & - \\  & selected features & radial basis function & \(2^{4}\) & \(2^{-13}\) & - \\ \hline \multirow{2}{*}{coriander} & all features & radial basis function & \(2^{-5}\) & \(2^{-15}\) & - \\  & selected features & radial basis function & \(2^{3}\) & \(2^{-9}\) & - \\ \hline \multirow{2}{*}{fir} & all features & radial basis function & \(2^{-9}\) & \(2^{-15}\) & - \\  & selected features & radial basis function & \(2^{2}\) & \(2^{-6}\) & - \\ \hline \multirow{2}{*}{honeydew} & all features & polynomial & \(2^{4}\) & - & 6 \\  & selected features & polynomial & \(2^{3}\) & - & 7 \\ \hline \multirow{2}{*}{raspberry} & all features & polynomial & \(2^{2}\) & - & 1 \\  & selected features & radial basis function & \(2^{3}\) & \(2^{-9}\) & - \\ \hline \multirow{2}{*}{sunflower} & all features & polynomial & \(2^{2}\) & - & 4 \\  & selected features & polynomial & \(2^{-3}\) & - & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configuration of the developed support vector machines models for each honey variety.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.