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Modeling the formation of some polycyclic aromatic hydrocarbons during the roasting of Arabica coffee samples

机译:模拟阿拉比卡咖啡烘焙过程中一些多环芳烃的形成

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Roasting is a critical process in coffee production, as it enables the development of flavor and aroma. At the same time, roasting may lead to the formation of nondesirable compounds, such as polycyclic aromatic hydrocarbons (PAHs). In this study, Arabica green coffee beans from Cuba were roasted under controlled conditions to monitor PAH formation during the roasting process. Roasting was performed in a pilot-spouted bed roaster, with the inlet air temperature varying from 180 to 260 degrees C, for roasting conditions ranging from 5 to 20 min. Several PAHs were determined in both roasted coffee samples and green coffee samples. Different models were tested, with more or less assumptions on the chemical phenomena, with a view to predict the system global behavior. Two kinds of models were used and compared: kinetic models (based on Arrhenius law) and statistical models (neural networks). The numbers of parameters to adjust differed for the tested models, varying from three to nine for the kinetic models and from five to 13 for the neural networks. Interesting results are presented, with satisfactory correlations between experimental and predicted concentrations for some PAHs, such as pyrene, benz[a]anthracene, chrysene, and anthracene.
机译:烘焙是咖啡生产中的关键过程,因为它可以促进风味和香气的发展。同时,烘烤可能会导致形成不良化合物,例如多环芳烃(PAH)。在这项研究中,在受控条件下烘焙了来自古巴的阿拉比卡咖啡豆,以监控烘焙过程中PAH的形成。在中试喷头式焙烧炉中进行焙烧,焙烧条件为5到20分钟,进风温度在180到260摄氏度之间变化。在烘焙咖啡样品和生咖啡样品中都测定了几种PAH。测试了不同的模型,或多或少地假设了化学现象,以预测系统的整体行为。使用和比较了两种模型:动力学模型(基于阿伦尼乌斯定律)和统计模型(神经网络)。对于测试模型,要调整的参数数量有所不同,对于动力学模型,参数的范围从3到9,对于神经网络,参数的数量从5到13。提出了有趣的结果,某些PAHs(例如and,苯并[a]蒽,蒽和蒽)的实验浓度与预测浓度之间具有令人满意的相关性。

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