## 3 Results and Discussion
It can be observed that coffees were mainly grouped by intensity levels, represented with different colors in the PCA. Contrary to all other samples, coffees of high-intensity levels (I11-I13) were mainly associated with phenols, which, as previously mentioned, are mainly the smoke aromas, which are formed during the roasting process. Figure 4: Principal component analysis biplot depicting the volatile aromatic compounds and coffee samples. Each vector color represents a functional group to which the compounds belong. Abbreviations of the volatile aromatic compounds can be found in Table 3 and samples in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Label** & **Compound** & **Functional Group** & **Aroma\({}^{\star}\)** & **Label** & **Common Name** & **Functional Group** & **Aroma\({}^{\star}\)** \\ \hline
**C1** & 3-Methyl/fluorene & Farms & Toxic compound & **C24** & 2-Acetyl-3-methyl/furan & Farms & Many/nuty/account/milky \\ \hline
**C2** & 2-S-Dimethylfuran & Farms & Masty/coffe// chocolate & **C25** & 2,2-Bitran & Farms & Medicinal/campher \\ \hline
**C3** & 2-Vinylfuran & Farms & Phenolic coffee grounds & **C26** & 2-Acetyl peroxide & Pyrrides & Cherry/liceice/salmt/broadly \\ \hline
**C4** & Pyridine & Other & Scour/smoke/coffe/bart/bart & **C27** & 2,3-Alfinitely-3-2 & & Other & Naturally found in _Interplay rib  
Figure 5 shows the matrix with significant correlations (\(p<0.05\)) between the volatile aromatic compounds grouped by functional groups from the GC-MS analysis and the results from the nine e-nose sensors. It can be observed that MQ3 sensor was positively correlated with furans (r = 0.42), phenols (r = 0.54), pyrroles (r = 0.55), and others (r = 0.60). The \"others\" group comprises compounds that belong to esters, pyridines, aromatic hydrocarbons, acetates, and ketones. Some compounds in the furans and other groups contain an alcohol functional group, explaining their correlation with the MQ3 sensor. Even though this sensor is most sensitive to alcohol, it also has a lower sensitivity to smoke, which may explain its correlation with phenols, as well as pyrroles and furans, which are composed of some compounds associated with smoke and burnt aromans [63]. MQ4 sensor was positively correlated with pyrroles (r = 0.40) and others (r = 0.47); this sensor is mainly sensitive to methane and butane but also has a lower sensitivity to alcohols and smoke, which may explain these correlations. On the contrary, MQ7 was negatively correlated with furans (r = -0.54) and pyrroles (r = -0.60). Furthermore, sensors MQ135, MQ136, MQ137 and MQ138 were negatively correlated with aldehydes (r = -0.44--0.49) and positively correlated with furans (r = 0.44-0.54), phenols (r = 0.57-0.63), pyrroles (r = 0.58-0.67) and others (r = 0.50-0.58). Sensor MG811 was negatively correlated with furans (r = -0.40). Given the correlations between the e-nose outputs and GC-MS results, two ML models were developed using the e-nose outputs as inputs, as described in Section 2.4. Table 4 shows the accuracy of Model 1 to predict coffees' intensity level as low, medium, and high. It can be observed that the overall accuracy was 98% with training and testing stages resulting in 100% and 94%, respectively. The lower training MSE value (<0.01) compared to the testing stage (MSE = 0.04) confirms that there were no signs of under- or overfitting of the model. Figure 6 shows the overall receiver operating characteristics (ROC) curve in which the three categories are very close to the highest sensitivity (true positive rate). Figure 5: Matrix showing the significant correlations (\(p<0.05\)) between the volatile aromatic compounds functional groups and the sensors integrated into the electronic nose. Correlation coefficients are depicted with different colors according to the color bar in which yellow represents the negative correlations, while blue shows the positive correlations.