Cvetanović Kljakić Aleksandra, Radosavljević Miloš, Zengin Gokhan, Yan Linlin, Gašić Uroš, Kojić Predrag, Torbica Aleksandra, Belović Miona, Zeković Zoran
Faculty of Technology, University of Novi Sad, Bulevar Cara Lazara 1, 21000 Novi Sad, Serbia.
Department of Biology, Science Faculty, Selcuk University, Konya 42130, Turkey.
Plants (Basel). 2023 Mar 7;12(6):1211. doi: 10.3390/plants12061211.
Chamomile is one of the most consumed medicinal plants worldwide. Various chamomile preparations are widely used in various branches of both traditional and modern pharmacy. However, in order to obtain an extract with a high content of the desired components, it is necessary to optimize key extraction parameters. In the present study, optimization of process parameters was performed using the artificial neural networks (ANN) model using a solid-to-solvent ratio, microwave power and time as inputs, while the outputs were the yield of the total phenolic compounds (TPC). Optimized extraction conditions were as follows: a solid-to-solvent ratio of 1:80, microwave power of 400 W, extraction time of 30 min. ANN predicted the content of the total phenolic compounds, which was later experimentally confirmed. The extract obtained under optimal conditions was characterized by rich composition and high biological activity. Additionally, chamomile extract showed promising properties as growth media for probiotics. The study could make a valuable scientific contribution to the application of modern statistical designs and modelling to improve extraction techniques.
洋甘菊是全球消费最多的药用植物之一。各种洋甘菊制剂在传统药学和现代药学的各个分支中都有广泛应用。然而，为了获得所需成分含量高的提取物，有必要优化关键提取参数。在本研究中，使用人工神经网络（ANN）模型进行工艺参数优化，以固液比、微波功率和时间作为输入，而输出则是总酚类化合物（TPC）的产率。优化后的提取条件如下：固液比为1:80，微波功率为400 W，提取时间为30分钟。人工神经网络预测了总酚类化合物的含量，随后通过实验得到证实。在最佳条件下获得的提取物具有成分丰富和生物活性高的特点。此外，洋甘菊提取物作为益生菌的生长培养基表现出良好的特性。该研究可为应用现代统计设计和建模来改进提取技术做出有价值的科学贡献。
