Volatolomics Profiling of Specialty Green Coffee Beans from South and Central American Harvesting Sites

Abstract

Understanding and analyzing coffee volatiles is essential for quality control, authenticity verification, and product development. To qualify as specialty coffee, the beans must not only achieve this score but also meet specific quality standards throughout the supply chain, which includes factors such as the growing conditions, processing methods, and overall flavour profile. This study investigates volatile organic compound (VOCs) profiles for further geographical authentication of green specialty coffee (Coffea arabica L.) samples using GC-MS, hierarchical cluster analysis, and principal component analysis (PCA). The dissimilarity dendrogram revealed distinct clustering patterns with intra-regional distances ranging from 13.97 to 16.10 units for Brazilian samples and inter-regional distances of 40.03–44.17 units between geographical groups. PCA of ten VOC classes demonstrated that the first two principal components account for 51.93% of the total variance. The clustering effectiveness was supported by a cophenetic correlation coefficient of 0.55, indicating moderate reliability in geographical discrimination. PCA revealed that furans, hydrocarbons, and heterocyclic compounds contributed positively to PC1, while alcohols showed strong positive loading (0.518) on PC2. The plot indicates that five principal components (factors) explain 87.48% of the total variance, suggesting complex VOC interactions in geographical differentiation. The significant overlap observed in the PCA analysis suggests shared characteristics between regions, which may be attributed to similar agricultural practices or cultivars.