## 3 Results and Discussion
The high-resolution power and sensitivity of gas chromatography (GC), together with the structural information provided by mass spectrometry (MS), has made the coupling GC-MS the technique of choice for the qualitative and quantitative analysis of food volatiles [93,94]. However, the preliminary steps of fractionation from major non-volatile components of the honey matrix (sugars and water) and preconcentration are still necessary for the chromatographic separation of honey volatiles. In the purge-and-trap (P&T) technique, volatiles swept by a flow of inert gas are trapped on an adsorbent, while further thermal/solvent desorption of the trap allows volatiles to enter the chromatographic system for separation. Furthermore, in P&T, the reduction in or elimination of organic solvents is achieved, offering a green alterative to sample preparation, combined with simultaneous multiclass compound extraction and reproducibility associated with a totally automated system [25]. The MANOVA that was applied for the compounds found in 100% of honey samples of each honey type showed significant differences among the honeys for some characteristic compounds, such as 1-octene (C20), benzaldehyde (C40), and nonanal (C80) (Table 1). In turn, a linear discriminant analysis through the stepwise method (SLDA) was used in order to determine whether the significant volatile compounds could discriminate the examined monofloral honey types. The first canonical variable corresponded to 58.9%, whereas the second corresponded to 25.5% of variation (Table 2). Additionally, regarding the group centroids of the first function, fir honeys had a mean of \(-10.623\), pine honeys a mean of \(-2.250\), erica honeys a mean of \(21.424\), thymne honeys a mean of \(0.918\), and cotton honeys a mean of \(-19.655\). All the monofloral honey types were discriminated, and this was more obvious for erica and cotton honeys because they were located in the most distant places (Figure 4), which was also confirmed by the different chromatograms that these two honey types presented, compared with the other studied monofloral honey types (Figure 2). Samples of the same honey type were located at the same quadrant, concentrated around the centroid of the respective honey type, showing the large correlation inside the same group (Figure 4). Additionally, the stepwise method was applied to determine the compounds that will be used in the prediction model. The original group cases were correctly classified at a 94.2% rate, whereas the cross-validated group cases were correctly classified at a 92.3% rate (Table 3). Five F scores were calculated (Table 4) and used for the classification in the group membership of five samples (test set) whose botanical origin was known (\"reference samples\") for verification of the predictive model. The samples were assigned to the group for which the classification function had the largest F score (Table 4). For all the reference samples, the prediction was correct. The combination of chemometric approaches, such as principal component analysis (PCA), cluster analysis (CA), or discriminant analysis (DA), with the analysis of volatile compounds for the characterization of other monofloral honey types studied by other authors had also led to promising results for the correct classification of honey samples [32; 95; 96]. However, due to the complexity of volatile compounds' structure that makes them unstable along with the various methods of analysis used for their determination, a larger number of honey samples could optimize the prediction model, and thus, the honey authentication.