## 4 Discussion  
In this study, a PID detector was used to discriminate between two varieties of rose-mary plants based on VOCs emission. To our knowledge, this is the first study on the use of a PID used for the particular purpose of discriminating different varieties in the field of aromatic plants. PID is one of the most popular techniques on the market for the detection of VOCs in the ppb range [20]. Figure 6: Graphic visualization of the neural network in ANN. Figure 7: ANN learning cycle parameters. Our approach was to use a PID detector as a quick, easy, and relatively inexpensive method for the classification of different varieties of rosemary plants. In the current literature, some studies have addressed the same topic using more complex technologies. [PERSON] et al. [21] reported a rapid discrimination between grape cultivars (_Vitis vinifera_ L.) using e-nose technology with a high detection accuracy using five chemometric methods, including PCA, LDA, QDA, SVM, and ANN. In other study [22], different potato cultivars have been studied using e-nose technology with regard to VOC emission. Again, the results showed high detection accuracy and the cultivars were precisely identified. Similar results have been obtained by [PERSON] et al. [23] concerning the classification of essential oils in _Rosa damascena_ Mill. using an e-nose. In our study, the strategy was simpler and a single PID detector was used. The idea was that signal kinetics in the time domain might be a function of the morphological (shape, dimension, histological classification) and physiological (essential oil composition) characteristics of rosemary plants. Our hypothesis was that the temporal kinetics of VOC emissions (emanation) from rosemary leaves have the potential to be a fingerprint for specific varieties. Therefore, the first result of this study was to ensure that the PID was capable of measuring VOC emissions from rosemary leaves, and encouraging results were obtained. The data collected from the VOC emission readings of the two rosemary varieties were analyzed and well-fitted by an exponential model described by three parameters with a corrected R\({}^{2}\) of 0.9956 and a standard error of estimate of 0.0119. All three parameters describing the model were significantly different from zero at \(p\leq\) 0.001 and the ANOVA of the regression was significant at the same level. This could be an important achievement in light of the current trend to seek innovative solutions for VOC monitoring and detection based on the use of small, portable instruments that are immediate and easy to read [24]. The ANOVA identified a significant main effect with respect to qualitative aspects of the two varieties of rosemary plants. In general, significant differences were seen between the two varieties for all of the reported compounds. Both the identified compounds and their percentages are consistent with the results reported in the current literature [25; 26]. The results obtained allowed us to state that the two varieties used for the study did indeed have two different varietal profiles. Thus, the second encouraging result was that different signals were recorded for the two varieties. This demonstrated that it was possible to obtain a sort of \"fingerprint\" for each variety studied. As the two varieties were correctly identified, this confirmed that the PID can be used for classification purposes, which was the main aim of the present study. Furthermore, data analyses using different statistical models were satisfactory. Here, the aim was to interpret the dataset recorded by the PID. The two unsupervised methods (PCA and CA) were able to distinguish the two varieties. The PCA accurately differentiated the two varieties: 82.59% of total variance was explained by F1 and F2 (60.59% and 22.00%, respectively). The variables that were most influential in distinguishing the two varieties were MAX, GM, AUC and GMsd (for F1), and \(\alpha\), \(\beta\), and \(\gamma\), (for F2). The same result was achieved by CA.