# Potential Aroma Chemical Fingerprint of Oxidised Coffee Note by HS-SPME-GC-MS and Machine Learning
## 3 Results and Discussion
### Extraction of the Informative Volatiles of the Oxidised Note  
Supervised PLS-DA machine learning was applied to the whole sample set to extract informative volatiles that describe the oxidised coffee note. The samples were split in a training set (\(n=45\)) and a validation set (\(n=15\)). The training set model was cross-validated  
Figure 1: Heat map of the chemical normalized IS fingerprints of the good and oxidised moka samples (M). Samples are clustered using ascendant hierarchical clustering based on Euclidean distances using the average linkage agglomerative method. MG (moka good coffee) and MOX (moka oxidised coffees). Figure 2: PCA of all samples investigated: (**a**) good and oxidised samples, 2, (**b**) moka good and oxidised samples, (**c**) blends in Eco-caps. by jackknife (CV = 5) and the model recomputing after removing each group from the training set, one by one. The cumulative Q\({}^{2}\) statistics was 0.886 on the first two components with a classification rate of 93.33%. Table 2 lists the 71 significant volatiles, out 147, with a VIP (variable importance in projection) > 1 in describing the oxidised samples. VIP scores estimate the importance of variables in the projection used in the PLS-DA model and are often used to select variables; when a variable presents a VIP that is close to or greater than 1, it can be considered important in the given model. Table 2 also reports the VIP standard deviations and boundaries at a 95% confidence limit, the Pearson correlation coefficient (r), and descriptions of odour quality. Most volatiles present high r, and volatiles with an r > 0.7 are strongly correlated with oxidation. The components with 0.5 < r > 0.7 are moderately correlated and are more linked to the packaging. These data show that several volatiles are more abundant in oxidised samples, and some, in bold, are common to all packagings. Although coffee aroma is the result of the synergistic effect between several volatiles, some of the substances that correlate better with oxidation have been described as having an unpleasant odour. This result means that several components change independently of the packaging and blends, although to different extents. Figure S1 shows the percentage coefficients of variation (CV%) of the normalized responses of the good _versus_ oxidised samples in the different packages. The figure shows variations above 20%, which is the relative standard deviation of the analytical method (RSD% > 20), whose limits are indicated in the figure by the orange horizontal dashed lines. The CV% was calculated using Equation (1):  
\[\text{CV}\%=-[(\text{good norm. response}-\text{oxidised norm. response})/\text{good norm. response}]\times 100 \tag{1}\]  
Moka samples (Figure S1a) show a significant reduction in aroma components compared with the capsules (Figure S1b-d), especially in the volatile components associated with freshness notes, such as sulphurious compounds (thiols), pyrazines, pyrroles, and alcohols; and this fact could be related to the higher surface exposure of the matrix to potential oxidation and humidity effects [33]. On the other hand, the composition of the volatile fraction of coffee in capsules varies less, probably because it is packaged under a modified atmosphere. Comparison of the Informative Volatiles in Oxidised Coffees and Literature Data on Markers of Coffee Ageing  
Several studies have investigated the change in coffee aroma over time, and most have focused on the loss of freshness (i.e., staling) and/or have monitored the evolution of aroma over a short period [34], with only a few investigating long-term effects [7; 20]. Table S1 lists the volatiles that have been reported in previous studies as being linked to the deterioration of roasted coffee aroma. Few volatiles characterising the oxidised coffees in our samples have already been described in the literature as ageing markers.