## 3 Results and Discussion
### Discriminant Analysis of EC Samples  
Linear discriminant analysis (LDA, after log transformation) was performed on the data of the chemical classes of volatile compounds quantified in the 162 total coffee samples, with the aim of discriminating them by geographical origin. The results of the analysis are shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Robusta**} & \multicolumn{4}{c}{**Arabica**} \\ \cline{2-7}  & **RD \({}^{2}\)** & **INDIA Cherry** & **VIETNAM** & **UCSADA** & **PENU** & **COLOMBIA** & **BRAZIL** \\ \hline \multirow{3}{*}{3-CQA (mg/L)} & L & 34.156 \(\pm\) 3.127 c & 33.682 \(\pm\) 0.381 c & 34.644 \(\pm\) 4.791 c & 50.858 \(\pm\) 3.913 bA & 62.483 \(\pm\) 4.806 a & 48.756 \(\  
The chemiometric model obtained by LDA showed a \(\lambda\)Wilks value 0.000, suggesting that the group means were different. The LDA (probability of F to remove 0.10 and to enter 0.05) selected all chemical classes as discriminant of two discriminant factors, based on standardized canonical discriminant function coefficients, for FD1 (83.19%): aldehydes (2.149), esters (\(-1.536\)), sulphur compounds (\(-1.054\)), N-heterocycle (\(-0.892\)), phenols (0.549), pyrazines (0.516), alcohols (\(-0.386\)), ketones (0.359), furaus (\(-0.208\)), pyrimidine (0.190) and acids (0.091); and for FD2 (7.48%): ketones (2.093), N-heterocycle (\(-1.609\)), aldehydes (\(-1.460\)), pyrazines (\(-0.699\)), esters (0.697), phenols (0.522), alcohols (\(-0.391\)), sulphur compounds (0.370), furaus (0.121), pyrimidine (\(-0.071\)) and acids (0.066).  
The LDA showed 98.15% correctly classified samples. The model was leave-one-out cross-validate, obtaining 97.53% correctly validated samples, and the confusion matrix for the cross-validated results is shown in Table 2. Apart from samples D_VIETNAM and D_COLUMBIA, all other samples were correctly classified, leading to a total correct classification of 97.53%. Specifically, D_VIETNAM was misclassified as D_COLUMBIA twice, and vice versa. Previously, [PERSON] et al. [13] reported the correct classification of coffee samples according to geographical origin of 82.61%. The authors focused on nine green coffee Arabica samples from Africa, South and Central America, for a total of 23 samples, using the chemical classes of volatile compounds as model parameters for LDA analysis.  
In our study, we examined nine samples from Brazil, Peru, Colombia, India, Vietnam and Uganda, also investigating the effect of three different roasting levels. The score plot (Figure 2) showed a clear separation not only for the origin of the samples, but also for the different roasting degree, suggesting that the volatile compounds were effective in discriminating both origin and roasting level. Interestingly, the lower the level of roasting, the more evident the separation of the samples according to their geographical origin. Therefore, the light roasting showed a better separation than dark roasting, where the latter was the cause of misclassification for the D_VIETNAM and D_COLUMBIA samples. The increase in the level of roasting of the coffee beans led to the development of new volatile compounds, such as furans and sulfur compounds, which reduced the differences originally present in the green coffee beans, and higher classification errors occur in ECs with higher roasting levels [14], corroborating our findings of better classification for light- and medium-roasted samples.  
Figure 2: Identification of geographical origin by LDA using the chemical classes of volatile compounds quantified in the 162 EC samples. L = light; M = medium; D = dark.