## 2 Results and Discussion
### Non-Targeted Analysis of Two Coffee Groups  
#### 2.2.1 Classification Model and Feature Selection  
To account for the instrumental variability, all sample triplicates were considered for the implementation of the classification model, leading therefore to a total of \(n=48\) observations and 630 features. The model was generated using the TreeBagger function in Matlab that implements the RF classification algorithm [30]. RF was chosen because it performs well for high-dimensional datasets, where the number of features outweighs the number of observations [31; 32]. Its ability to overcome the dimensionality issue is attributed to the fact that only a subset of the features are considered to build each decision tree. The misclassification probability of the trained classifier is computed using the out-of-bag error (oobError). Owing to the calculation of the oobError, it can be considered that the TreeBagger algorithm has a built-in support for cross-validation, hence its implementation does not require splitting the data into training and validation sets. The classifier oobError was equal to 0, suggesting that all samples were accurately classified. Even though the TreeBagger algorithm has comparable functionalities to cross-validation, to endorse the accuracy of the obtained classifier, the dataset was randomly partitioned into a 60% training set (\(n=29\)) and a 40% validation set (\(n=19\)). In this scheme, the oobError of the training set was equal to 0.0690 and the classification model exhibited very high accuracy, as shown in the corresponding confusion matrix (Figure 6a). In order to select the features that best divide the data, predictor importance is estimated based on the impact that the permutation of out-of-bag predictor observations has on the Mean Squared Error (MSE). In other words, the values for each individual feature are permuted across all the observations in the selected dataset. After the permutation, the MSE is computed. The larger this value, the more important the feature. The OOBPermutedVarDeltaError Matlab function was used to assess feature importance. It calculates the MSE averaged over all trees in the classification ensemble and divides it by the standard deviation (SD) taken over the trees, for each variable. Figure 2: (a) PCA scores plot of the regular and the decaffeinated coffees using 52 aroma-related volatiles data set. (b) The selected features which significantly differed between the two coffee groups by performing the \(t\)-test and (c) PLS-DA. The numbers in the feature column of the \(t\)-test table and the \(y\)-axis of the PLS-DA VIP scores graph indicate the numbering of the aroma-related volatiles. (d) The volatile compounds corresponding to the significant features. A cutoff of 0.35 was set to select the 20 most discriminatory features (Figure 6b). These features belonged to different chemical families and included five pyrazines, five furans, two alcohols, two thiazoles, one oxolane, one methylxanthine, one triazole, one phenol and one pyrrole. The features names and numbers are listed in the Supplementary Materials Section (Table S1). Some of these chemical families were already investigated in the targeted analysis section (Section 2.1). Nonetheless, a major presence of pyrazine- and furan-derived compounds was observed among these features. Additionally, the efficiency of the selected features in accurately dividing the complete data set into two distinct groups (regular and decaf) was confirmed using PCA score plots (Figure 6c,d). It is worth noting that, based on Figure 6d, the presence of three subgroups in the decaffeinated coffee samples was suspected. Their presence was confirmed by performing a PCA only on the decaffeinated coffee samples, as shown in Figure S1 in the Supplementary Materials Section. The main difference between these groups consisted of the used decaffeination method. In fact, all the samples in group I belong to the same vendor, and caffeine was removed using the same method (LiCO\({}_{2}\)/water).