Messias Evandro, Guimarães Cleidiana V, da Luz José M R, de Paulo Ellisson H, Oliveira Emanuele C da S, Nascimento Márcia H C, Ferrão Marco F, Guarçoni Rogério C, Filgueiras Paulo R, Pereira Lucas L
Laboratory of Research and Development of Methodologies for Analysis of Oils - LABPETRO, Chemistry Department, Federal University of Espírito Santo, Av. Fernando Ferrari, 514, Vitória, Espírito Santo 29075-910, Brazil.
Federal University of Viçosa, Department of Microbiology, Laboratory of Mycorrhizal Associations - LAMIC, Avenida P.H. Rolfs S/N, Viçosa, Minas Gerais-MG 36570-900, Brazil.
Food Chem. 2025 Oct 15;489:144907. doi: 10.1016/j.foodchem.2025.144907. Epub 2025 May 28.
The sensory attributes of Coffea canephora beverages depend on the chemical composition of the bean, especially to the content of volatile organic compounds (VOCs). However, the relative abundance of these compounds may vary with the stage of bean maturation. This study investigated the VOCs responsible for the sensory attributes of Coffea canephora after malting and fermentation with Saccharomyces cerevisiae using HS-SPME-GC-MS and random forest analysis with synthetic sampling. Ninety-four VOCs were identified, of which approximately 10 % contributed to discriminating the sensory profiles of the beverage. After 64 h of fermentation, malting treatments using fructose (T2), glucose (T3), and cellulase (T4) increased the sensory scores compared to natural coffee. The maceration/fermentation of coffee berries generated intense microbial activity, favoring the generation of VOCs. The machine learning methods proved efficient in identifying VOCs. Odor activity values demonstrated that the VOCs identified by this method were relevant to the sensory profile of coffee beverage.
卡内弗拉咖啡饮品的感官特性取决于咖啡豆的化学成分，尤其是挥发性有机化合物（VOCs）的含量。然而，这些化合物的相对丰度可能会随着咖啡豆成熟阶段的不同而有所变化。本研究采用顶空固相微萃取-气相色谱-质谱联用（HS-SPME-GC-MS）以及合成采样的随机森林分析方法，探究了经麦芽处理和酿酒酵母发酵后的卡内弗拉咖啡中负责其感官特性的挥发性有机化合物。共鉴定出94种挥发性有机化合物，其中约10%有助于区分该饮品的感官特征。发酵64小时后，与天然咖啡相比，使用果糖（T2）、葡萄糖（T3）和纤维素酶（T4）进行麦芽处理提高了感官评分。咖啡浆果的浸渍/发酵产生了强烈的微生物活性，有利于挥发性有机化合物的生成。机器学习方法在识别挥发性有机化合物方面被证明是有效的。气味活性值表明，通过该方法鉴定出的挥发性有机化合物与咖啡饮品的感官特征相关。
