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Development of an Artificial Neural Network as a Tool for Predicting the Targeted Phenolic Profile of Grapevine (Vitis vinifera) Foliar Wastes
High performance liquid chromatography data related to the concentrations of 12 phenolic compounds in vegetative parts, measured at four sampling times were processed for developing prediction models, based on the cultivar, grapevine organ, growth stage, total flavonoid content (TFC), total reducing...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6018394/ https://www.ncbi.nlm.nih.gov/pubmed/29971086 http://dx.doi.org/10.3389/fpls.2018.00837 |
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author | Eftekhari, Maliheh Yadollahi, Abbas Ahmadi, Hamed Shojaeiyan, Abdolali Ayyari, Mahdi |
author_facet | Eftekhari, Maliheh Yadollahi, Abbas Ahmadi, Hamed Shojaeiyan, Abdolali Ayyari, Mahdi |
author_sort | Eftekhari, Maliheh |
collection | PubMed |
description | High performance liquid chromatography data related to the concentrations of 12 phenolic compounds in vegetative parts, measured at four sampling times were processed for developing prediction models, based on the cultivar, grapevine organ, growth stage, total flavonoid content (TFC), total reducing capacity (TRC), and total antioxidant activity (TAA). 12 Artificial neural network (ANN) models were developed with 79 input variables and different number of neurons in the hidden layer, for the prediction of 12 phenolics. The results confirmed that the developed ANN-models (R(2) = 0.90 – 0.97) outperform the stepwise regression models (R(2) = 0.05 – 0.78). Moreover, the sensitivity of the model outputs against each input variable was computed by using ANN and it was revealed that the key determinant of phenolic concentration was the source organ of the grapevine. The ANN prediction technique represents a promising approach to predict targeted phenolic levels in vegetative parts of the grapevine. |
format | Online Article Text |
id | pubmed-6018394 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-60183942018-07-03 Development of an Artificial Neural Network as a Tool for Predicting the Targeted Phenolic Profile of Grapevine (Vitis vinifera) Foliar Wastes Eftekhari, Maliheh Yadollahi, Abbas Ahmadi, Hamed Shojaeiyan, Abdolali Ayyari, Mahdi Front Plant Sci Plant Science High performance liquid chromatography data related to the concentrations of 12 phenolic compounds in vegetative parts, measured at four sampling times were processed for developing prediction models, based on the cultivar, grapevine organ, growth stage, total flavonoid content (TFC), total reducing capacity (TRC), and total antioxidant activity (TAA). 12 Artificial neural network (ANN) models were developed with 79 input variables and different number of neurons in the hidden layer, for the prediction of 12 phenolics. The results confirmed that the developed ANN-models (R(2) = 0.90 – 0.97) outperform the stepwise regression models (R(2) = 0.05 – 0.78). Moreover, the sensitivity of the model outputs against each input variable was computed by using ANN and it was revealed that the key determinant of phenolic concentration was the source organ of the grapevine. The ANN prediction technique represents a promising approach to predict targeted phenolic levels in vegetative parts of the grapevine. Frontiers Media S.A. 2018-06-19 /pmc/articles/PMC6018394/ /pubmed/29971086 http://dx.doi.org/10.3389/fpls.2018.00837 Text en Copyright © 2018 Eftekhari, Yadollahi, Ahmadi, Shojaeiyan and Ayyari. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Eftekhari, Maliheh Yadollahi, Abbas Ahmadi, Hamed Shojaeiyan, Abdolali Ayyari, Mahdi Development of an Artificial Neural Network as a Tool for Predicting the Targeted Phenolic Profile of Grapevine (Vitis vinifera) Foliar Wastes |
title | Development of an Artificial Neural Network as a Tool for Predicting the Targeted Phenolic Profile of Grapevine (Vitis vinifera) Foliar Wastes |
title_full | Development of an Artificial Neural Network as a Tool for Predicting the Targeted Phenolic Profile of Grapevine (Vitis vinifera) Foliar Wastes |
title_fullStr | Development of an Artificial Neural Network as a Tool for Predicting the Targeted Phenolic Profile of Grapevine (Vitis vinifera) Foliar Wastes |
title_full_unstemmed | Development of an Artificial Neural Network as a Tool for Predicting the Targeted Phenolic Profile of Grapevine (Vitis vinifera) Foliar Wastes |
title_short | Development of an Artificial Neural Network as a Tool for Predicting the Targeted Phenolic Profile of Grapevine (Vitis vinifera) Foliar Wastes |
title_sort | development of an artificial neural network as a tool for predicting the targeted phenolic profile of grapevine (vitis vinifera) foliar wastes |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6018394/ https://www.ncbi.nlm.nih.gov/pubmed/29971086 http://dx.doi.org/10.3389/fpls.2018.00837 |
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