Perrone Daniel, Donangelo Raul, Donangelo Carmen M, Farah Adriana
Laboratório de Bioquímica Nutricional e de Alimentos, Universidade Federal do Rio de Janeiro, Instituto de Química, Departamento de Bioquímica, Ilha do Fundão, CT bloco A, 21949-900 Rio de Janeiro, RJ, Brazil.
J Agric Food Chem. 2010 Dec 8;58(23):12238-43. doi: 10.1021/jf102110u. Epub 2010 Nov 4.
Roasting is a key step in the production of a high-quality coffee. Roasting degree is directly related to coffee chemical composition and may be determined objectively by weight loss after roasting. Chlorogenic acids (CGA) are thermally labile phenolic compounds that play an important role in the final cup quality and health benefits of coffee. Considering the interest in finding a reliable method to predict weight loss and CGA content in coffee, models have been developed to estimate these parameters during roasting. Weight loss was successfully modeled (r = 0.99) independent of the instant temperature. CGA degradation followed first-order Arrhenius-compliant kinetic models with good predictability (r = 0.98), especially for light to moderately dark samples. In both cases distinct models for Coffea arabica and Coffea canephora were calculated, because of differences in chemical composition and cell wall structure between these species. The proposed models may become important predictive tools in the coffee industry.
烘焙是生产高品质咖啡的关键步骤。烘焙程度与咖啡的化学成分直接相关，并且可以通过烘焙后的失重客观地确定。绿原酸（CGA）是热不稳定的酚类化合物，对咖啡的最终品质和健康益处起着重要作用。考虑到人们对寻找一种可靠方法来预测咖啡失重和CGA含量的兴趣，已开发出模型来估算烘焙过程中的这些参数。失重成功建模（r = 0.99），与即时温度无关。CGA降解遵循符合一级阿累尼乌斯动力学模型，具有良好的可预测性（r = 0.98），特别是对于浅度至中度深度烘焙的样品。由于阿拉比卡咖啡和卡内弗拉咖啡在化学成分和细胞壁结构上存在差异，在这两种情况下都分别计算了各自的模型。所提出的模型可能成为咖啡行业重要的预测工具。
