Colzi Ilaria, Taiti Cosimo, Marone Elettra, Magnelli Susanna, Gonnelli Cristina, Mancuso Stefano
Department of Agri-Food and Environmental Science, Università di Firenze, via delle Idee 30, Sesto Fiorentino, 50019 Firenze, Italy.
Faculty of Biosciences and Technologies for Agriculture, Food and Environment, Università di Teramo, via R. Balzarini 1, 64100 Teramo, Italy.
Food Chem. 2017 Dec 15;237:257-263. doi: 10.1016/j.foodchem.2017.05.071. Epub 2017 May 17.
This work was performed to evaluate the possible application of PTR-ToF-MS technique in distinguishing between Coffea arabica (Arabica) and Coffea canephora var. robusta (Robusta) commercial stocks in each step of the processing chain (green beans, roasted beans, ground coffee, brews). volatile organic compounds (VOC) spectra from coffee samples of 7 Arabica and 6 Robusta commercial stocks were recorded and submitted to multivariate statistical analysis. Results clearly showed that, in each stage of the coffee processing, the volatile composition of coffee is highly influenced by the species. Actually, with the exception of green beans, PTR-ToF-MS technique was able to correctly recognize Arabica and Robusta samples. Particularly, among 134 tentatively identified VOCs, some masses (16 for roasted coffee, 12 for ground coffee and 12 for brewed coffee) were found to significantly discriminate the two species. Therefore, headspace VOC analyses was showed to represent a valuable tool to distinguish between Arabica and Robusta.
本研究旨在评估质子转移反应-飞行时间质谱（PTR-ToF-MS）技术在咖啡加工链各环节（生豆、烘焙豆、研磨咖啡、冲泡咖啡）区分阿拉比卡咖啡（Arabica）和卡内弗拉咖啡变种罗布斯塔咖啡（Robusta）商业库存方面的潜在应用。记录了来自7种阿拉比卡和6种罗布斯塔商业库存咖啡样品的挥发性有机化合物（VOC）光谱，并进行多变量统计分析。结果清楚地表明，在咖啡加工的每个阶段，咖啡的挥发性成分都受到品种的高度影响。实际上，除了生豆外，PTR-ToF-MS技术能够正确识别阿拉比卡和罗布斯塔样品。特别是，在134种初步鉴定的VOC中，发现一些质量数（烘焙咖啡中有16个，研磨咖啡中有12个，冲泡咖啡中有12个）能够显著区分这两个品种。因此，顶空VOC分析被证明是区分阿拉比卡和罗布斯塔的一种有价值的工具。
Zotero 插件