Saffariha Maryam, Jahani Ali, Jahani Reza
College of Natural Resources University of Tehran Tehran Iran.
Assessment and Environment Risks Department Research Center of Environment and Sustainable Development Tehran Iran.
Plant Direct. 2021 Nov 24;5(11):e363. doi: 10.1002/pld3.363. eCollection 2021 Nov.
Hyperforin, a major bioactive constituent of concentration, is impacted by various phenological phases and soil characteristics. We aimed to design a model predicting hyperforin content in based on different ecological and phenological conditions. We employed artificial intelligence modeling techniques including multilayer perceptron (MLP), radial basis function (RBF), and support vector machine (SVM) to examine the factors critical in predicting hyperforin content. We found that the MLP model ( = .9) is the most suitable and precise model compared with RBF ( = .81) and SVM ( = .74) in predicting hyperforin in based on ecological conditions, plant growth, and soil features. Moreover, phenological stages, organic carbon, altitude, and total N are detected in sensitivity analysis as the main factors that have a considerable impact on hyperforin content. We also report that the developed graphical user interface would be adaptable for key stakeholders including producers, manufacturers, analytical laboratory managers, and pharmacognosists.
金丝桃素是贯叶连翘的一种主要生物活性成分，其含量受不同物候期和土壤特性的影响。我们旨在设计一个基于不同生态和物候条件预测贯叶连翘中金丝桃素含量的模型。我们采用了包括多层感知器（MLP）、径向基函数（RBF）和支持向量机（SVM）在内的人工智能建模技术，来研究预测金丝桃素含量的关键因素。我们发现，基于生态条件、植物生长和土壤特征，在预测贯叶连翘中的金丝桃素时，MLP模型（R2 = 0.9）比RBF模型（R2 = 0.81）和SVM模型（R2 = 0.74）更合适、更精确。此外，在敏感性分析中，检测到物候期、有机碳、海拔和总氮是对金丝桃素含量有相当大影响的主要因素。我们还报告称，所开发的图形用户界面将适用于包括生产者、制造商、分析实验室管理人员和生药学家在内的关键利益相关者。
