## 4 Conclusions  
The state-of-the-art HS-SPME-GC\(\times\)GC-TOFMS instrument has made it possible to obtain the fingerprint chromatogram of a natural product, such as coffee in this study. It further enabled both targeted and non-targeted analysis to distinguish the decaffeinated coffees from regular ones. Two coffee groups were separated by 52 key aroma-related volatiles. In particular, the pyrazines showed significantly reduced content in decaffeinated coffee due to the removal of sucrose together with caffeine during the decaffeination process. The reduction in pyrazines can explain the thin aroma of decaffeinated coffees due to the lack of nutty, roasted and chocolate notes. For the non-targeted analysis, the use of the random forest machine-learning algorithm enabled the selection of 20 discriminatory features, among which a major presence of pyrazine and furan derivatives were noticed. These features allowed for an accurate classification of the studied coffee samples. Additionally, pyrazines were identified to be a marker of the regular coffee group, whereas furans were depicted as a marker of the decaffeinated coffee group. The highlighted differences in the presence of certain volatile compounds in the two coffee types along with their potential impact on the coffee aroma profile can be of great interest for the coffee industry. Understanding why and how certain volatile compounds are less concentrated in decaffeinated coffee can help coffee vendors to improve the decaffeination process to shorten the gap between the aroma profile of decaffeinated coffee and regular coffee. A pilot study on conducting the decaffeination of the green coffee beans using the main methods, specifically water-based, solvent-based, and liquid/supercritical carbon dioxide techniques, will be interesting in the future. The decaffeination effect on roasted coffee aroma by different decaffeination techniques will also be worth investigating. The approaches applied in this study to distinguish decaffeinated coffee from regular coffee could also be applied to other types of natural products. However, it is worth mentioning that applying them to study more complex natural products or to establish multiple classifications could be more challenging. **Supplementary Materials:** The following are available online at [[https://www.mdpi.com/article/10.3390/molecules27061806/s1](https://www.mdpi.com/article/10.3390/molecules27061806/s1)]([https://www.mdpi.com/article/10.3390/molecules27061806/s1](https://www.mdpi.com/article/10.3390/molecules27061806/s1)), Table S1: The top 20 discriminatory features selected using the random forest algorithm. Table S2: The analytical condition of HS-SPME-GC\(\times\)GC-TOFMS. Figure S1: PCA score plots of the decaffeinated coffee samples. Figure S2: The correlation matrix between the top 20 features. Figure S3: The ROC curve of the validation set (\(n=30\)) generated by using the Random forest algorithm. Figure S4: The 2D chromatogram of coffee R-R.  
**Author Contributions:** Conceptualization, Y.Z. and M.G. ; methodology, Y.Z., M.G. and F.A.F. ; software, Y.Z., M.G. and P.-H.S. ; validation, Y.Z. and M.G. ; formal analysis, Y.Z. and M.G. ; investigation, Y.Z. ; resources, Y.Z., M.G. and J.-F.F. ; data curation, Y.Z. and P.-H.S. ; writing--original draft preparation, Y.Z. and M.G. ; writing--review and editing, Y.Z., M.G., F.A.F., P.-H.S. and J.-F.F. ; visualization, Y.Z. and M.G. ; supervision, P.-H.S. and J.-F.F. ; project administration, Y.Z. ; funding acquisition, J.-F.F. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the FWO/FNRS Belgium EOS Grant 30897864 \"Chemical Information Mining in a Complex World\". **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author. **Acknowledgments:** Y.Z.