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Comparison of Artificial Neural Networks and Response Surface Methodology towards an Efficient Ultrasound-Assisted Extraction of Chlorogenic Acid from Lonicera japonica

Chlorogenic acid (CGA), a bioactive compound commonly found in plants, has been demonstrated possessing nutraceutical potential in recent years. However, the more critical issue concerning how to improve production efficacy of CGA is still limited. It is a challenge to harvest a large amount of CGA...

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Autores principales: Yu, Hui-Chuan, Huang, Shang-Ming, Lin, Wei-Min, Kuo, Chia-Hung, Shieh, Chwen-Jen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6631501/
https://www.ncbi.nlm.nih.gov/pubmed/31234365
http://dx.doi.org/10.3390/molecules24122304
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author Yu, Hui-Chuan
Huang, Shang-Ming
Lin, Wei-Min
Kuo, Chia-Hung
Shieh, Chwen-Jen
author_facet Yu, Hui-Chuan
Huang, Shang-Ming
Lin, Wei-Min
Kuo, Chia-Hung
Shieh, Chwen-Jen
author_sort Yu, Hui-Chuan
collection PubMed
description Chlorogenic acid (CGA), a bioactive compound commonly found in plants, has been demonstrated possessing nutraceutical potential in recent years. However, the more critical issue concerning how to improve production efficacy of CGA is still limited. It is a challenge to harvest a large amount of CGA without prolonging extraction time. In this study, the feasibility of using ultrasound for CGA extraction from Lonicera japonica was investigated. A central composite design (CCD) was employed to evaluate the effects of the operation parameters, including temperature, ethanol concentration, liquid to solid ratio, and ultrasound power on CGA yields. Meanwhile, the process of ultrasound-assisted extraction was optimized through modeling response surface methodology (RSM) and artificial neural network (ANN). The data indicated that CGA was efficiently extracted from the flower of Lonicera japonica by ultrasound assistance. The optimal conditions for the maximum extraction of CGA were as follows: The temperature at 33.56 °C, ethanol concentration at 65.88%, L/S ratio at 46:1 mL/g and ultrasound power at 150 W. ANN possessed greater optimization capacity than RSM for fitting experimental data and predicting the extraction process to obtain a maximum CGA yield. In conclusion, the process of ultrasound-assisted extraction can be well established by a methodological approach using either RSM or ANN, but it is worth mentioning that the ANN model used here showed the superiority over RSM for predicting and optimizing.
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spelling pubmed-66315012019-08-19 Comparison of Artificial Neural Networks and Response Surface Methodology towards an Efficient Ultrasound-Assisted Extraction of Chlorogenic Acid from Lonicera japonica Yu, Hui-Chuan Huang, Shang-Ming Lin, Wei-Min Kuo, Chia-Hung Shieh, Chwen-Jen Molecules Article Chlorogenic acid (CGA), a bioactive compound commonly found in plants, has been demonstrated possessing nutraceutical potential in recent years. However, the more critical issue concerning how to improve production efficacy of CGA is still limited. It is a challenge to harvest a large amount of CGA without prolonging extraction time. In this study, the feasibility of using ultrasound for CGA extraction from Lonicera japonica was investigated. A central composite design (CCD) was employed to evaluate the effects of the operation parameters, including temperature, ethanol concentration, liquid to solid ratio, and ultrasound power on CGA yields. Meanwhile, the process of ultrasound-assisted extraction was optimized through modeling response surface methodology (RSM) and artificial neural network (ANN). The data indicated that CGA was efficiently extracted from the flower of Lonicera japonica by ultrasound assistance. The optimal conditions for the maximum extraction of CGA were as follows: The temperature at 33.56 °C, ethanol concentration at 65.88%, L/S ratio at 46:1 mL/g and ultrasound power at 150 W. ANN possessed greater optimization capacity than RSM for fitting experimental data and predicting the extraction process to obtain a maximum CGA yield. In conclusion, the process of ultrasound-assisted extraction can be well established by a methodological approach using either RSM or ANN, but it is worth mentioning that the ANN model used here showed the superiority over RSM for predicting and optimizing. MDPI 2019-06-21 /pmc/articles/PMC6631501/ /pubmed/31234365 http://dx.doi.org/10.3390/molecules24122304 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yu, Hui-Chuan
Huang, Shang-Ming
Lin, Wei-Min
Kuo, Chia-Hung
Shieh, Chwen-Jen
Comparison of Artificial Neural Networks and Response Surface Methodology towards an Efficient Ultrasound-Assisted Extraction of Chlorogenic Acid from Lonicera japonica
title Comparison of Artificial Neural Networks and Response Surface Methodology towards an Efficient Ultrasound-Assisted Extraction of Chlorogenic Acid from Lonicera japonica
title_full Comparison of Artificial Neural Networks and Response Surface Methodology towards an Efficient Ultrasound-Assisted Extraction of Chlorogenic Acid from Lonicera japonica
title_fullStr Comparison of Artificial Neural Networks and Response Surface Methodology towards an Efficient Ultrasound-Assisted Extraction of Chlorogenic Acid from Lonicera japonica
title_full_unstemmed Comparison of Artificial Neural Networks and Response Surface Methodology towards an Efficient Ultrasound-Assisted Extraction of Chlorogenic Acid from Lonicera japonica
title_short Comparison of Artificial Neural Networks and Response Surface Methodology towards an Efficient Ultrasound-Assisted Extraction of Chlorogenic Acid from Lonicera japonica
title_sort comparison of artificial neural networks and response surface methodology towards an efficient ultrasound-assisted extraction of chlorogenic acid from lonicera japonica
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6631501/
https://www.ncbi.nlm.nih.gov/pubmed/31234365
http://dx.doi.org/10.3390/molecules24122304
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