# A Predictive Model to Correlate Amino Acids and Aromatic Compounds in Calabrian Honeys
## 3 Results and Discussion  
To understand the flavor and aroma of honey [35], it is therefore necessary to know the characteristics and the correlation between the main molecules that affect the bouquet of aroma that will be formed in honey. Through the building of a predictive model, a method for correlating amino acid profiles to aromatic compounds was developed. The objective of \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Compound** & **Precursor (\(m/z\))** & **Product (\(m/z\))** & **Dwell (msec)** & **Q1 (V)** & **Ce** & **Q3 (V)** \\ \hline Asparagine & 133 & 74 & 22 & \(-14\) & \(-15\) & \(-30\) \\  & 133.1 & 87.1 & 22 & \(-12\) & \(-12\) & \(-18\) \\ \hline Glutammine & 147.1 & 84 & 22 & \(-16\) & \(-15\) & \(-22\) \\  & 130 & 130 & 22 & \(-16\) & \(-24\) & \(-26\) \\ \hline Tryptophan & 204.9 & 118.1 & 8 & \(-22\) & \(-24\) & \(-25\) \\  & 204.9 & 118.1 & 8 & \(-22\) & \(-10\) & \(-21\) \\ \hline Cistyne & 241.2 & 74.1 & 13 & \(-10\) & \(-26\) & \(-29\) \\  & 240.8 & 122 & 100 & \(-11\) & \(-16\) & \(-26\) \\ \hline Hydroxyproline & 132.2 & 74.2 & 100 & \(-17\) & \(-23\) & \(-29\) \\  & 123.1 & 86.1 & 100 & \(-15\) & \(-14\) & \(-18\) \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._this study was to enhance our comprehension of the intricate realm of honey taste and its overall acceptability. The model was based on data derived from the analysis (Figure 1) of the amino acid profile using a HILIC-UPLC-ESI-MS/MS and from the analysis of the aromatic profile using HS-SPME-GC-MS [36; 37]. Through this study, a highly promising HILIC method was established, enabling the swift and accurate quantification of 17 amino acids frequently encountered in honey samples. Additionally, the application of SPME-GC-MS facilitated an in-depth exploration of the aromatic profile, leading to the identification of 130 distinct aromatic compounds. In an effort to examine the interrelationship between amino acids and volatile compounds (aroma), a chemometric approach was employed. This allowed for a systematic analysis of the correlations between these components. A correlation analysis (Pearson test) was used to make the comparisons among and between values for the single amino acid content and classes of aromatic compounds (aldehydes, alcohols, ketones, furans, hydrocarbons, and esters) (Table 2). A Pearson's product-moment correlation coefficient, denoted as \"_r_\", serves as a prevalent measure of association. This coefficient is dimensionless and evaluates the linear relationship between two variables. Consider a scenario where there are two  
Figure 1: Citrus honey: (**A**) Amino acids profile by HILIC-UPLC-ESI-MS/MS (from the top down: Hydroxyproline, Arginine, Aspartic Acid, Lysine, Histidine, Cysteine, Glutamine, Asparagine, Glutamic Acid, Alanine, Tyrosine, Serine, Phenyalalanine, Leucine, Threonine, Methionine, Valine, Proline, Glycine, Isooleucine, Tryptophan) (**B**) Volatile profile by HS-SPME-GC-MS of Citrus Honey. continuous traits, \(X\) and \(Y\), measured for each of the \(N\) individuals in a sample. In such a situation, the calculation of the Pearson's correlation coefficient involves:  
\[\overline{X}=\frac{1}{N}\sum_{i=1}^{N}X_{i}\]  
\[\overline{Y}=\frac{1}{N}\sum_{i=1}^{N}Y_{i}\]  
\[r=\frac{\sum_{i=1}^{N}\left[\left(X_{i}-\overline{X}\right)\left(Y_{i}- \overline{Y}\right)\right]}{\sqrt{\sum_{i=1}^{N}\left(X_{i}-\overline{X} \right)^{2}}\sqrt{\sum_{i=1}^{N}\left(Y_{i}-\overline{Y}\right)^{2}}}\]  
The use of [PERSON]'s correlation coefficient (\(r\)) as a measure of association is based on several assumptions. These include: the individuals within the sample are assumed to be statistically independent of one another, and the population from which the sample was drawn is expected to exhibit a normal bivariate distribution for both of the features under consideration. Simple linear regression and correlation are both methods for investigating a potential linear relationship between two variables. They provide insights into a scatterplot's characteristics.