Food and Nutrition Graduate Program, the Federal University of State of Rio de Janeiro, Av. Pasteur, 296, 22290-240 Rio de Janeiro, Brazil.
Graduate Program in Food Science and Technology, Federal Rural University of Rio de Janeiro (PPGCTA-UFRRJ), Rodovia Br 465, km 7, 23890-000 Seropédica, Brazil.
Food Chem. 2024 Jul 15;446:138862. doi: 10.1016/j.foodchem.2024.138862. Epub 2024 Feb 29.
Roasted ground coffee has been intentionally adulterated for economic revenue. This work aims to use an untargeted strategy to process SPME-GC-MS data coupled with chemometrics to identify volatile compounds (VOCs) as possible markers to discriminate Arabica coffee and its main adulterants (corn, barley, soybean, rice, coffee husks, and Robusta coffee). Principal Component Analysis (PCA) showed the difference between roasted ground coffee and adulterants, while the Hierarchical Clustering of Principal Components (HCPC) and heat map showed a trend of adulterants separation. The partial Least-Squares Discriminant Analysis (PLS-DA) approach confirmed the PCA results. Finally, 24 VOCs were putatively identified, and 11 VOCs are candidates for potential markers to detect coffee fraud, found exclusively in one type of adulterant: coffee husks, soybean, and rice. The results for possible markers may be suitable for evaluating the authenticity of ground-roasted coffee, thus acting as a coffee fraud control and prevention tool.
烘焙咖啡豆被故意掺假以获取经济利益。本工作旨在使用非靶向策略处理 SPME-GC-MS 数据，并结合化学计量学方法来识别挥发性化合物 (VOCs)，作为区分阿拉比卡咖啡及其主要掺杂物（玉米、大麦、大豆、大米、咖啡壳和罗布斯塔咖啡）的可能标志物。主成分分析 (PCA) 显示了烘焙咖啡豆和掺杂物之间的差异，而主成分分层聚类 (HCPC) 和热图显示了掺杂物分离的趋势。偏最小二乘判别分析 (PLS-DA) 方法证实了 PCA 的结果。最后，推测出 24 种 VOCs，11 种 VOCs 可能是检测咖啡欺诈的潜在标志物，仅在一种掺杂物中发现：咖啡壳、大豆和大米。这些可能标志物的结果可能适合评估磨碎烘焙咖啡的真实性，从而成为咖啡欺诈控制和预防的工具。
