TY - JOUR
T1 - Prediction of hypericin content in Hypericum perforatum L. in different ecological habitat using artificial neural networks
AU - Saffariha, Maryam
AU - Jahani, Ali
AU - Jahani, Reza
AU - Latif, Sajid
N1 - Funding Information:
We are grateful to the College of Environment in National Department of Environment in Iran for partially cooperation in data collection. We are thankful to our colleagues who provided expertise that greatly assisted the research.
Publisher Copyright:
© 2021, The Author(s).
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/1/26
Y1 - 2021/1/26
N2 - Background: Hypericum is an important genus in the family Hypericaceae, which includes 484 species. This genus has been grown in temperate regions and used for treating wounds, eczema and burns. The aim of this study was to predict the content of hypericin in Hypericum perforatum in varied ecological and phenological conditions of habitat using artificial neural network techniques [MLP (Multi-Layer Perceptron), RBF (Radial Basis Function) and SVM (Support Vector Machine)]. Results: According to the results, the MLP model (R2 = 0.87) had an advantage over RBF (R2 = 0.8) and SVM (R2 = 0.54) models and it was relatively accurate in predicting hypericin content in H. perforatum based on the ecological conditions of site including soil types, its characteristics and plant phenological stages of habitat. The results of sensitivity analysis revealed that phenological stages, hill aspects, total nitrogen, altitude and organic carbon are the most influential factors that have an integral effect on the content of hypericin. Conclusions: The designed graphical user interface will help pharmacognosist, manufacturers and producers of medicinal plants and so on to run the MLP model on new data to easily discover the content of hypericin in H. perforatum by entering ecological conditions of site, soil characteristics and plant phenological stages.
AB - Background: Hypericum is an important genus in the family Hypericaceae, which includes 484 species. This genus has been grown in temperate regions and used for treating wounds, eczema and burns. The aim of this study was to predict the content of hypericin in Hypericum perforatum in varied ecological and phenological conditions of habitat using artificial neural network techniques [MLP (Multi-Layer Perceptron), RBF (Radial Basis Function) and SVM (Support Vector Machine)]. Results: According to the results, the MLP model (R2 = 0.87) had an advantage over RBF (R2 = 0.8) and SVM (R2 = 0.54) models and it was relatively accurate in predicting hypericin content in H. perforatum based on the ecological conditions of site including soil types, its characteristics and plant phenological stages of habitat. The results of sensitivity analysis revealed that phenological stages, hill aspects, total nitrogen, altitude and organic carbon are the most influential factors that have an integral effect on the content of hypericin. Conclusions: The designed graphical user interface will help pharmacognosist, manufacturers and producers of medicinal plants and so on to run the MLP model on new data to easily discover the content of hypericin in H. perforatum by entering ecological conditions of site, soil characteristics and plant phenological stages.
KW - Artificial neural network
KW - Ecological modeling
KW - Graphical user interface
KW - Hypericin
KW - Hypericum perforatum
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U2 - 10.1186/s13007-021-00710-z
DO - 10.1186/s13007-021-00710-z
M3 - Article
C2 - 33499873
AN - SCOPUS:85099825835
VL - 17
SP - 1
EP - 17
JO - Plant Methods
JF - Plant Methods
SN - 1746-4811
IS - 1
M1 - 10
ER -