## 3 Results and Discussion
Table 5 shows that Model 2 had a very high accuracy in the training, validation and testing stages based on the correlation coefficient (R = 0.99, R = 0.98, and R = 0.99, respectively), with an overall accuracy R = 0.99. This, along with the lower training MSE value (6.3 \(\times\) 1010) compared to the validation and testing stages, shows no signs of under- or overfitting. Furthermore, all stages presented a high slope close to the unity (b \(\sim\)1). Figure 7 shows the overall regression model with each volatile aromatic compound depicted using a different marker and color, in which it can be observed that furfuryl acetate appears as the highest peak area, as confirmed in Table S1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Stage** & **Samples** & **Accuracy** & **Error** & **Performance (MSE)** \\ \hline Training & 189 & 100\% & 0\% & \textless{}0.01 \\ Testing & 81 & 94\% & 6\% & 0.04 \\ Overall & 270 & 98\% & 2\% & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results from the artificial neural network pattern recognition model (Model 1) to classify coffee samples according to the intensity level, showing the accuracy and error of each stage. Performance was assessed based on mean squared error (MSE). Figure 6: Overall receiver operating characteristics (ROC) curve showing the true positive (sensitivity; y-axis) and false-positive (specificity; x-axis) rates of the classification Model 1. These models showed that the portable and low-cost e-nose coupled with ML is a reliable and effective tool to assess coffee intensity levels and the relative abundance of 45 different volatile aromatic compounds. Even though there are already published studies using e-noses to assess coffee aromas, and some also include the use of ML, they have been implemented in different ways. [PERSON] et al. [33] used an e-nose coupled with ANN to predict sensory descriptors; however, the authors used an \(\alpha\)FOX4000 (Alpha M.O.S., Toulouse, France) commercial, non-portable and high-cost e-nose. [PERSON] et al. [34] used a similar e-nose along with ML for a more specific and limited use to classify samples into civet or non-civet. The e-nose these authors presented only had five sensors that are sensitive to gases such as CO\({}_{2}\), methane, CO, and natural gas; however, they did not include any sensor related to faulty aromas such as H\({}_{2}\)O, NH\({}_{3}\) and aromatics such as benzene, which are included in the present paper and have shown to be effective in detecting more aromas in beverages such as beer and wine [16; 17]. [PERSON] et al. [35] presented a method to predict the roasting time of coffee using a PEN2 (Airsense Analytics, Milano, Italy) e-nose, which, although it claims to be portable, is still quite large and requires a bench, and consumables such as specific vials to function. Besides this, the authors presented an ANN regression model to predict the roasting time; however, their models, although with high determination coefficients (R\({}^{2}\) > 0.96), only had eight data points, which is not sufficient for any regression model. The method presented in this study has some advantages over other similar proposed techniques, as mentioned above. Further advantages include that the e-nose presented in this study is portable, wireless, and low-cost compared to other commercial e-noses and GC/MS equipment methods. Furthermore, the e-nose and models are validated with robust technology, such as the GC/MS, verifying its high accuracy and objective results. The fact that the e-nose is portable and fully wireless makes it more convenient for transporting the device and measuring samples in any location with the potential to be used in the field to be used to assess coffee beans. A disadvantage of the proposed method would be the need for GC/MS data used as ground-truth to develop further models, including additional samples with different characteristics to generate a more universal artificial intelligence model accounting coffee provenance, brands, water temperature and hardness effect, among others.