A comparison of artificial intelligence techniques for predicting hyperforin content in Hypericum perforatum L. in different ecological habitats

Hyperforin, a major bioactive constituent of Hypericum concentration, is impacted by various phenological phases and soil characteristics. We aimed to design a model predicting hyperforin content in Hypericum perforatum 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 (R-2 = .9) is the most suitable and precise model compared with RBF (R-2 = .81) and SVM (R-2 = .74) in predicting hyperforin in H. perforatum 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.

Automated summary

This study focuses on predicting hyperforin content in Hypericum perforatum using artificial intelligence techniques, with the MLP model found to be the most suitable. Phenological stages, organic carbon, altitude, and total N are key factors influencing hyperforin content. The developed graphical user interface is adaptable for various stakeholders.