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 artificia...

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Bibliographic Details
Published in:Plant direct 2021-11, Vol.5 (11), p.e363-n/a
Main Authors: Saffariha, Maryam, Jahani, Ali, Jahani, Reza
Format: Article
Language:English
Subjects:
Altitude
Artificial intelligence
Chromatography
ecological modeling
Environmental conditions
graphical user interface
Herbal medicine
hyperforin
Hypericum perforatum
Mass spectrometry
Metabolites
Neural networks
Original Research
Pharmacognosy
Scientific imaging
Sensitivity analysis
Sensors
Soil characteristics
Support vector machines
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Summary: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 (R2 = .9) is the most suitable and precise model compared with RBF (R2 = .81) and SVM (R2 = .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.
ISSN:2475-4455
2475-4455
DOI:10.1002/pld3.363