Đurović Saša, Pezo Lato, Gašić Uroš, Gorjanović Stanislava, Pastor Ferenc, Bazarnova Julia G, Smyatskaya Yulia A, Zeković Zoran
Laboratory of Chromatography, Institute of General and Physical Chemistry, Studentski trg 12/V, 11158 Belgrade, Serbia.
Graduate School of Biotechnology and Food Industries, Peter the Great Saint-Petersburg Polytechnic University, Polytechnicheskaya Street, 29, 195251 Saint-Petersburg, Russia.
Foods. 2023 Feb 14;12(4):809. doi: 10.3390/foods12040809.
Stinging nettle ( L.) is one fantastic plant widely used in folk medicine, pharmacy, cosmetics, and food. This plant's popularity may be explained by its chemical composition, containing a wide range of compounds significant for human health and diet. This study aimed to investigate extracts of exhausted stinging nettle leaves after supercritical fluid extraction obtained using ultrasound and microwave techniques. Extracts were analyzed to obtain insight into the chemical composition and biological activity. These extracts were shown to be more potent than those of previously untreated leaves. The principal component analysis was applied as a pattern recognition tool to visualize the antioxidant capacity and cytotoxic activity of extract obtained from exhausted stinging nettle leaves. An artificial neural network model is presented for the prediction of the antioxidant activity of samples according to polyphenolic profile data, showing a suitable anticipation property (the value during the training cycle for output variables was 0.999).
荨麻(L.)是一种了不起的植物,在民间医学、制药、化妆品和食品领域有着广泛应用。这种植物广受欢迎或许可以归因于其化学成分,它含有多种对人体健康和饮食具有重要意义的化合物。本研究旨在调查采用超声和微波技术进行超临界流体萃取后得到的荨麻叶耗尽提取物。对提取物进行分析以深入了解其化学成分和生物活性。结果表明,这些提取物比未经处理的叶子提取物更具效力。主成分分析作为一种模式识别工具,用于可视化从荨麻叶耗尽提取物中获得的抗氧化能力和细胞毒性活性。根据多酚谱数据,提出了一种人工神经网络模型来预测样品的抗氧化活性,显示出良好的预测性能(训练周期内输出变量的 值为0.999)。