# A Predictive Model to Correlate Amino Acids and Aromatic Compounds in Calabrian Honeys
## 3 Results and Discussion
Regression analysis is suitable when examining the relationship between \(X\) and \(Y\) when they are functionally dependent. Functional dependence implies that there's an asymmetry between the variables, where Y can be expressed as a function of \(X\). This approach is useful for predicting between variables. Functional dependence does not necessarily require identifying a cause-and-effect mechanism; it focuses on predicting one variable from another. On the other hand, calculating a correlation coefficient is valuable for measuring the degree of association between variables, regardless of whether regression is appropriate or not. The magnitude of the correlation coefficient (\(r\)) does not indicate the slope of the linear trend curve; rather, it quantifies the probable dispersion of a group of individuals around this trend line. The value of r always falls between \(-1\) and \(1\). A value of \(r=0\) signifies no linear association between the variables. A value of \(r=1\) indicates a perfect positive linear relationship, where all sampled individuals align on the same positive-sloped straight line. If \(0<r<1\), a positive linear trend exists, but the sampled individuals exhibit dispersion around the common trend line. Smaller absolute values of r \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Aroma Compounds Classes** & **Amino Acids** & **Pearson Correlation Coefficient** & \(p\) **Value** & **Correlation Curve** \\ \hline \multirow{4}{*}{Aldehydes} & Isoleucine & 0.931 & \(<\)0.0001 & y = 0.827x \(-\) 0.0151 \\  & Valine & 0.922 & \(<\)0.0001 & y = 0.810x \(-\) 0.0151 \\  & Leucine & 0.932 & \(<\)0.0001 & y = 0.8276x \(-\) 0.0151 \\  & Phenylalanine & 0.972 & \(<\)0.0001 & y = 0.8631x \(-\) 0.0151 \\ \hline \multirow{4}{*}{Furans} & Isoleucine & 0.982 & \(<\)0.0001 & y = 0.8188x \(-\) 0.0852 \\  & Leucine & 0.982 & \(<\)0.0001 & y = 0.8186x \(-\) 0.0852 \\  & Phenylalanine & 0.983 & \(<\)0.0001 & y = 0.8194x \(-\) 0.0852 \\ \hline \multirow{4}{*}{Hydrocarbons} & Serine & 0.793 & \(<\)0.0001 & y = 0.7894x \(+\) 0.0461 \\  & Glutamic Acid & 0.941 & \(<\)0.0001 & y = 0.9552x \(-\) 0.1389 \\  & Asparatic Acid & 0.888 & \(<\)0.0001 & y = 0.8971x \(-\) 0.1019 \\ \hline \multirow{4}{*}{Ketones} & Serine & 0.905 & \(<\)0.0001 & y = 0.9127x \(+\) 0.0116 \\  & Alanine & 0.929 & \(<\)0.0001 & y = 0.99364x \(+\) 0.0116 \\  & Glutamine & \(-\)0.964 & \(<\)0.0001 & y = \(-\)0.9715x \(+\) 0.0116 \\  & Histidine & \(-\)0.979 & \(<\)0.0001 & y = \(-\)0.99867x \(+\) 0.0116 \\  & Lysine & \(-\)0.958 & \(<\)0.0001 & y = \(-\)0.9655x \(+\) 0.0116 \\  & Asparagine & \(-\)0.964 & \(<\)0.0001 & y = \(-\)0.9714x \(+\) 0.0116 \\ \hline \multicolumn{2}{c}{Alcohols} & Tyrosine & 0.927 & \(<\)0.0001 & y = 1.1056x \(+\) 0.0364 \\ \hline \multicolumn{2}{c}{Esters} & Arginine & \(-\)0.98 & \(<\)0.0001 & y = \(-\)1.00631x \(+\) 0.0992 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Pearson correlations among amino acids and aroma compounds (classes) in honey samples. imply less data cohesion around a single linear relationship. A positive r suggests that an increase in one variable corresponds to an expected increase in the other. A value of \(r=-1\) suggests a perfect negative relationship where sampled individuals consistently adhere to the same negatively sloped linear trend line [38]. In this work, which was considered good, the value of the Pearson correlation coefficient was never lower than 0.75.