# A Predictive Model to Correlate Amino Acids and Aromatic Compounds in Calabrian Honeys
## 3 Results and Discussion
In this specific scenario, the honey samples act as additional variables. In the generated PLS regression map (Figure 3), the correlation circle illustrates the relationships among honey types, aromatic classes, and amino acid variables with respect to the first two PLS components. The graphical layout is divided into four sections by two bisectors, and each region corresponds to one specific honey type. Additionally, within each area, the associated aromatic compounds and amino acids that are positively correlated with that particular honey type are indicated. The quality of the regression is deemed satisfactory: the overall R-squared value (R2) between Y and (t1, t2) is calculated as 0.632, while the cross-validated R-squared value (Q2 cum) amounts to 0.539. In Figure 3, the positive correlations between chestnut and eucalyptus honey with hydrocarbons, esters, ketones, proline, alanine, serine, and aspartic acids and the negative correlations between the terpenes, asparagine, histidine, and lysine are shown. The Sulla honeys showed an opposite relationship. Citrus showed a positive correlation between alcohols, aldehydes, furans, valine, isoleucine, leucine, phenylalanine, and triosine; Acacia honey showed a negative correlation with the same molecules of citrus. Figure 3 shows that two amino acids (threonine and glutamic acid) cannot be related to product characteristics since they are located in the center of the graphical display. The model was a correlation expressed as a Pearson index between 17 amino acids and 130 volatile compounds grouped in different chemical families (aldehydes, furans, hydrocarbons, ketones, alcohols, and esters). The data obtained showed strong positive linear correlations between aldehydes and isoleucine, valine, leucine, and phenylalanine. Furans are correlated with isoleucine, leucine, and phenylalanine; hydrocarbons with serine, glutamic acid, and aspartic acid; and ketones with serine, alanine, glutamine, histidine, asparagine, and lysine. Alcohols were more associated with tyrosine than esters with arginine. Proline, tryptophan, and threonine showed poor correlations with all the classes of aroma compounds. This research opens avenues for enhanced traceability and  
Figure 3: Correlation circle of the honey type (green). aromatic classes (red) and amino acids (blue) (t1; t2). identification of honey based on its amino acid and aromatic composition. This feasibility study shows the excellent potentiality of the model as a modern analytical tool for a rapid multicomponent analysis of honey quality indicators. The utility of the developed predictive model is closely tied to the feasibility of obtaining information about honey using much simpler analytical protocols. For example, as demonstrated, gaining insights into the amino acid composition of honey requires the use of rather complex analytical methods due to the nature of the molecules and especially the matrix. Conversely, the analysis of the aromatic profile is straightforward: it does not demand excessive sample preparation and is also relatively cost-effective and environmentally friendly, as it does not require solvents. Through correlation modeling, the analysis of the aromatic profile has the potential to not only define the sensory quality of the product but also the amino acid composition. Additionally, it can provide information about the botanical origin, given that many amino acids are considered markers for identifying specific types of honey.