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

Abstract 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...

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Autores principales:	Maryam Saffariha, Ali Jahani, Reza Jahani
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	artificial intelligence ecological modeling graphical user interface hyperforin Hypericum perforatum Botany QK1-989
Acceso en línea:	https://doaj.org/article/29fb0757746e46b0a1e0bb712d1a880a
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spelling	oai:doaj.org-article:29fb0757746e46b0a1e0bb712d1a880a2021-11-29T07:25:55ZA comparison of artificial intelligence techniques for predicting hyperforin content in Hypericum perforatum L. in different ecological habitats2475-445510.1002/pld3.363https://doaj.org/article/29fb0757746e46b0a1e0bb712d1a880a2021-11-01T00:00:00Zhttps://doi.org/10.1002/pld3.363https://doaj.org/toc/2475-4455Abstract 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.Maryam SaffarihaAli JahaniReza JahaniWileyarticleartificial intelligenceecological modelinggraphical user interfacehyperforinHypericum perforatumBotanyQK1-989ENPlant Direct, Vol 5, Iss 11, Pp n/a-n/a (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial intelligence ecological modeling graphical user interface hyperforin Hypericum perforatum Botany QK1-989
spellingShingle	artificial intelligence ecological modeling graphical user interface hyperforin Hypericum perforatum Botany QK1-989 Maryam Saffariha Ali Jahani Reza Jahani A comparison of artificial intelligence techniques for predicting hyperforin content in Hypericum perforatum L. in different ecological habitats
description	Abstract 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.
format	article
author	Maryam Saffariha Ali Jahani Reza Jahani
author_facet	Maryam Saffariha Ali Jahani Reza Jahani
author_sort	Maryam Saffariha
title	A comparison of artificial intelligence techniques for predicting hyperforin content in Hypericum perforatum L. in different ecological habitats
title_short	A comparison of artificial intelligence techniques for predicting hyperforin content in Hypericum perforatum L. in different ecological habitats
title_full	A comparison of artificial intelligence techniques for predicting hyperforin content in Hypericum perforatum L. in different ecological habitats
title_fullStr	A comparison of artificial intelligence techniques for predicting hyperforin content in Hypericum perforatum L. in different ecological habitats
title_full_unstemmed	A comparison of artificial intelligence techniques for predicting hyperforin content in Hypericum perforatum L. in different ecological habitats
title_sort	comparison of artificial intelligence techniques for predicting hyperforin content in hypericum perforatum l. in different ecological habitats
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/29fb0757746e46b0a1e0bb712d1a880a
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A comparison of artificial intelligence techniques for predicting hyperforin content in Hypericum perforatum L. in different ecological habitats

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