Application of multivariate linear regression models for selection of deep eutectic solvent for extraction of apigenin and luteolin from Chrysanthemum indicum L

Introduction Among a variety of compounds presented in chrysanthemum, apigenin and luteolin are the two main components that play a major role in numerous biological activities of this herb. Objectives We aimed to obtain linear models showing the dependence of the yield of extraction of apigenin and...

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Veröffentlicht in:Phytochemical analysis 2022-04, Vol.33 (3), p.427-440
Hauptverfasser: Nguyen Thu, Hang, Vu Thi Huyen, Trang, Nguyen Van, Phuong
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Vu Thi Huyen, Trang
Nguyen Van, Phuong
description Introduction Among a variety of compounds presented in chrysanthemum, apigenin and luteolin are the two main components that play a major role in numerous biological activities of this herb. Objectives We aimed to obtain linear models showing the dependence of the yield of extraction of apigenin and luteolin on the composition of deep eutectic solvent and investigate the extraction of these two ingredients from Chrysanthemum indicum L. Methods Two models showing the dependence of luteolin and apigenin concentrations on the composition of the solvent were established using a multilinear regression algorithm and were applied to screen 119 different solvents. After that, the extraction process was optimized using response surface methodology and an artificial neural network. Apigenin and luteolin were recovered from the extract by the combination of distillation and addition of water. Results The screening results on 119 solvents revealed that choline chloride–acetic acid (1:4) was the most suitable deep eutectic solvent. It was showed that both response surface methodology and the artificial neural network could accurately determine the optimal conditions of extraction of apigenin and luteolin from C. indicum L., including time of extraction (65 minutes), temperature of extraction (90°C) and water content (20%). By the combination of distillation and addition of water, apigenin and luteolin could be effectively recovered from the deep eutectic solvent extract with a recovery rate of over 80%. Conclusions Deep eutectic solvent could be used as an effective green alternative to the conventional solvents for the extraction of bioactive compounds from plants. • The relationship between the apigenin/luteolin content and components of DES were obtained to select the most suitable solvents. • Both response surface methodology and artificial neural network were used to optimize the conditions of extraction. • The combination of distillation and addition of water was used to recover apigenin and luteolin from DES extract. • The interaction between apigenin, luteolin and the solvent were preliminarily studied using molecular dynamics simulation.
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Objectives We aimed to obtain linear models showing the dependence of the yield of extraction of apigenin and luteolin on the composition of deep eutectic solvent and investigate the extraction of these two ingredients from Chrysanthemum indicum L. Methods Two models showing the dependence of luteolin and apigenin concentrations on the composition of the solvent were established using a multilinear regression algorithm and were applied to screen 119 different solvents. After that, the extraction process was optimized using response surface methodology and an artificial neural network. Apigenin and luteolin were recovered from the extract by the combination of distillation and addition of water. Results The screening results on 119 solvents revealed that choline chloride–acetic acid (1:4) was the most suitable deep eutectic solvent. It was showed that both response surface methodology and the artificial neural network could accurately determine the optimal conditions of extraction of apigenin and luteolin from C. indicum L., including time of extraction (65 minutes), temperature of extraction (90°C) and water content (20%). By the combination of distillation and addition of water, apigenin and luteolin could be effectively recovered from the deep eutectic solvent extract with a recovery rate of over 80%. Conclusions Deep eutectic solvent could be used as an effective green alternative to the conventional solvents for the extraction of bioactive compounds from plants. • The relationship between the apigenin/luteolin content and components of DES were obtained to select the most suitable solvents. • Both response surface methodology and artificial neural network were used to optimize the conditions of extraction. • The combination of distillation and addition of water was used to recover apigenin and luteolin from DES extract. • The interaction between apigenin, luteolin and the solvent were preliminarily studied using molecular dynamics simulation.</description><identifier>ISSN: 0958-0344</identifier><identifier>EISSN: 1099-1565</identifier><identifier>DOI: 10.1002/pca.3099</identifier><identifier>PMID: 34808692</identifier><language>eng</language><publisher>England: Wiley Subscription Services, Inc</publisher><subject>Acetic acid ; Algorithms ; Apigenin ; Artificial neural networks ; Bioactive compounds ; C. indicum ; Choline ; Chrysanthemum ; deep eutectic solvent ; Deep Eutectic Solvents ; Distillation ; Distilled water ; Eutectic composition ; Linear Models ; Luteolin ; Moisture content ; Neural networks ; Plant Extracts ; quantitative relationship ; Regression analysis ; Regression models ; Response surface methodology ; Solvent extraction ; Solvents ; Water content</subject><ispartof>Phytochemical analysis, 2022-04, Vol.33 (3), p.427-440</ispartof><rights>2021 John Wiley &amp; Sons, Ltd.</rights><rights>2022 John Wiley &amp; Sons, Ltd.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3499-4353888c2ad9a42744b1678c10cd88a14bba6d15a421be5d0cadd6203fb7e3223</citedby><cites>FETCH-LOGICAL-c3499-4353888c2ad9a42744b1678c10cd88a14bba6d15a421be5d0cadd6203fb7e3223</cites><orcidid>0000-0002-1915-0565</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fpca.3099$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fpca.3099$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34808692$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Nguyen Thu, Hang</creatorcontrib><creatorcontrib>Vu Thi Huyen, Trang</creatorcontrib><creatorcontrib>Nguyen Van, Phuong</creatorcontrib><title>Application of multivariate linear regression models for selection of deep eutectic solvent for extraction of apigenin and luteolin from Chrysanthemum indicum L</title><title>Phytochemical analysis</title><addtitle>Phytochem Anal</addtitle><description>Introduction Among a variety of compounds presented in chrysanthemum, apigenin and luteolin are the two main components that play a major role in numerous biological activities of this herb. Objectives We aimed to obtain linear models showing the dependence of the yield of extraction of apigenin and luteolin on the composition of deep eutectic solvent and investigate the extraction of these two ingredients from Chrysanthemum indicum L. Methods Two models showing the dependence of luteolin and apigenin concentrations on the composition of the solvent were established using a multilinear regression algorithm and were applied to screen 119 different solvents. After that, the extraction process was optimized using response surface methodology and an artificial neural network. Apigenin and luteolin were recovered from the extract by the combination of distillation and addition of water. Results The screening results on 119 solvents revealed that choline chloride–acetic acid (1:4) was the most suitable deep eutectic solvent. It was showed that both response surface methodology and the artificial neural network could accurately determine the optimal conditions of extraction of apigenin and luteolin from C. indicum L., including time of extraction (65 minutes), temperature of extraction (90°C) and water content (20%). By the combination of distillation and addition of water, apigenin and luteolin could be effectively recovered from the deep eutectic solvent extract with a recovery rate of over 80%. Conclusions Deep eutectic solvent could be used as an effective green alternative to the conventional solvents for the extraction of bioactive compounds from plants. • The relationship between the apigenin/luteolin content and components of DES were obtained to select the most suitable solvents. • Both response surface methodology and artificial neural network were used to optimize the conditions of extraction. • The combination of distillation and addition of water was used to recover apigenin and luteolin from DES extract. • The interaction between apigenin, luteolin and the solvent were preliminarily studied using molecular dynamics simulation.</description><subject>Acetic acid</subject><subject>Algorithms</subject><subject>Apigenin</subject><subject>Artificial neural networks</subject><subject>Bioactive compounds</subject><subject>C. indicum</subject><subject>Choline</subject><subject>Chrysanthemum</subject><subject>deep eutectic solvent</subject><subject>Deep Eutectic Solvents</subject><subject>Distillation</subject><subject>Distilled water</subject><subject>Eutectic composition</subject><subject>Linear Models</subject><subject>Luteolin</subject><subject>Moisture content</subject><subject>Neural networks</subject><subject>Plant Extracts</subject><subject>quantitative relationship</subject><subject>Regression analysis</subject><subject>Regression models</subject><subject>Response surface methodology</subject><subject>Solvent extraction</subject><subject>Solvents</subject><subject>Water content</subject><issn>0958-0344</issn><issn>1099-1565</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp10V1rHCEUBmApDc02KfQXFKE3uZlUR8d1LpelX7DQXiTXwxk9kxgcnepM0v03_alx81Uo9OqAPr4qLyHvOTvnjNWfJgPngrXtK7LiZVS8Uc1rsmJtoysmpDwmb3O-YazsteoNORZSM63aekX-bKbJOwOzi4HGgY6Ln90tJAczUu8CQqIJrxLmfBBjtOgzHWKiGT2a52MWcaK4zIcVQ3P0txjmB4a_5wQvDiZ3hcEFCsFSX3wsd9AhxZFur9M-Q5ivcVxG6oJ1pszdKTkawGd89zRPyOWXzxfbb9Xux9fv282uMkKWH0vRCK21qcG2IOu1lD1Xa204M1Zr4LLvQVnelD3eY2OZAWtVzcTQr1HUtTghZ4-5U4q_FsxzN7ps0HsIGJfc1Ypx2XIp1oV-_IfexCWF8rqipFKFNs3fQJNizgmHbkpuhLTvOOsOrXWlte7QWqEfngKXfkT7Ap9rKqB6BHfO4_6_Qd3P7eYh8B6TJKO6</recordid><startdate>202204</startdate><enddate>202204</enddate><creator>Nguyen Thu, Hang</creator><creator>Vu Thi Huyen, Trang</creator><creator>Nguyen Van, Phuong</creator><general>Wiley Subscription Services, Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QL</scope><scope>7QR</scope><scope>7T7</scope><scope>7TM</scope><scope>7U9</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>H94</scope><scope>K9.</scope><scope>M7N</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-1915-0565</orcidid></search><sort><creationdate>202204</creationdate><title>Application of multivariate linear regression models for selection of deep eutectic solvent for extraction of apigenin and luteolin from Chrysanthemum indicum L</title><author>Nguyen Thu, Hang ; Vu Thi Huyen, Trang ; Nguyen Van, Phuong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3499-4353888c2ad9a42744b1678c10cd88a14bba6d15a421be5d0cadd6203fb7e3223</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Acetic acid</topic><topic>Algorithms</topic><topic>Apigenin</topic><topic>Artificial neural networks</topic><topic>Bioactive compounds</topic><topic>C. indicum</topic><topic>Choline</topic><topic>Chrysanthemum</topic><topic>deep eutectic solvent</topic><topic>Deep Eutectic Solvents</topic><topic>Distillation</topic><topic>Distilled water</topic><topic>Eutectic composition</topic><topic>Linear Models</topic><topic>Luteolin</topic><topic>Moisture content</topic><topic>Neural networks</topic><topic>Plant Extracts</topic><topic>quantitative relationship</topic><topic>Regression analysis</topic><topic>Regression models</topic><topic>Response surface methodology</topic><topic>Solvent extraction</topic><topic>Solvents</topic><topic>Water content</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Nguyen Thu, Hang</creatorcontrib><creatorcontrib>Vu Thi Huyen, Trang</creatorcontrib><creatorcontrib>Nguyen Van, Phuong</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Chemoreception Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Phytochemical analysis</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Nguyen Thu, Hang</au><au>Vu Thi Huyen, Trang</au><au>Nguyen Van, Phuong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Application of multivariate linear regression models for selection of deep eutectic solvent for extraction of apigenin and luteolin from Chrysanthemum indicum L</atitle><jtitle>Phytochemical analysis</jtitle><addtitle>Phytochem Anal</addtitle><date>2022-04</date><risdate>2022</risdate><volume>33</volume><issue>3</issue><spage>427</spage><epage>440</epage><pages>427-440</pages><issn>0958-0344</issn><eissn>1099-1565</eissn><abstract>Introduction Among a variety of compounds presented in chrysanthemum, apigenin and luteolin are the two main components that play a major role in numerous biological activities of this herb. Objectives We aimed to obtain linear models showing the dependence of the yield of extraction of apigenin and luteolin on the composition of deep eutectic solvent and investigate the extraction of these two ingredients from Chrysanthemum indicum L. Methods Two models showing the dependence of luteolin and apigenin concentrations on the composition of the solvent were established using a multilinear regression algorithm and were applied to screen 119 different solvents. After that, the extraction process was optimized using response surface methodology and an artificial neural network. Apigenin and luteolin were recovered from the extract by the combination of distillation and addition of water. Results The screening results on 119 solvents revealed that choline chloride–acetic acid (1:4) was the most suitable deep eutectic solvent. It was showed that both response surface methodology and the artificial neural network could accurately determine the optimal conditions of extraction of apigenin and luteolin from C. indicum L., including time of extraction (65 minutes), temperature of extraction (90°C) and water content (20%). By the combination of distillation and addition of water, apigenin and luteolin could be effectively recovered from the deep eutectic solvent extract with a recovery rate of over 80%. Conclusions Deep eutectic solvent could be used as an effective green alternative to the conventional solvents for the extraction of bioactive compounds from plants. • The relationship between the apigenin/luteolin content and components of DES were obtained to select the most suitable solvents. • Both response surface methodology and artificial neural network were used to optimize the conditions of extraction. • The combination of distillation and addition of water was used to recover apigenin and luteolin from DES extract. • The interaction between apigenin, luteolin and the solvent were preliminarily studied using molecular dynamics simulation.</abstract><cop>England</cop><pub>Wiley Subscription Services, Inc</pub><pmid>34808692</pmid><doi>10.1002/pca.3099</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0002-1915-0565</orcidid></addata></record>
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source MEDLINE; Wiley Online Library Journals Frontfile Complete
subjects Acetic acid
Algorithms
Apigenin
Artificial neural networks
Bioactive compounds
C. indicum
Choline
Chrysanthemum
deep eutectic solvent
Deep Eutectic Solvents
Distillation
Distilled water
Eutectic composition
Linear Models
Luteolin
Moisture content
Neural networks
Plant Extracts
quantitative relationship
Regression analysis
Regression models
Response surface methodology
Solvent extraction
Solvents
Water content
title Application of multivariate linear regression models for selection of deep eutectic solvent for extraction of apigenin and luteolin from Chrysanthemum indicum L
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