Dimić Ivana, Pezo Lato, Rakić Dušan, Teslić Nemanja, Zeković Zoran, Pavlić Branimir
Faculty of Technology, University of Novi Sad, Blvd. Cara Lazara 1, 21000 Novi Sad, Serbia.
Institute of General and Physical Chemistry, University of Belgrade, Studentski Trg 12-16, 11000 Belgrade, Serbia.
Foods. 2021 Jun 30;10(7):1513. doi: 10.3390/foods10071513.
This study was primarily focused on the supercritical fluid extraction (SFE) of cherry seed oil and the optimization of the process using sequential extraction kinetics modeling and artificial neural networks (ANN). The SFE study was organized according to Box-Behnken design of experiment, with additional runs. Pressure, temperature and flow rate were chosen as independent variables. Five well known empirical kinetic models and three mass-transfer kinetics models based on the Sovová's solution of SFE equations were successfully applied for kinetics modeling. The developed mass-transfer models exhibited better fit of experimental data, according to the calculated statistical tests (, SSE and AARD). The initial slope of the SFE curve was evaluated as an output variable in the ANN optimization. The obtained results suggested that it is advisable to lead SFE process at an increased pressure and CO flow rate with lower temperature and particle size values to reach a maximal initial slope.
本研究主要聚焦于樱桃籽油的超临界流体萃取（SFE），以及使用序贯萃取动力学建模和人工神经网络（ANN）对该过程进行优化。SFE研究是根据Box-Behnken实验设计并增加了额外的运行来组织的。选择压力、温度和流速作为自变量。五个著名的经验动力学模型和三个基于索沃娃（Sovová）超临界流体萃取方程解的传质动力学模型成功应用于动力学建模。根据计算出的统计检验（、SSE和AARD），所开发的传质模型对实验数据的拟合效果更好。在人工神经网络优化中，将SFE曲线的初始斜率作为输出变量进行评估。所得结果表明，为达到最大初始斜率，建议在较高压力和CO流速下进行SFE过程，同时降低温度和颗粒尺寸值。
