Optimization of extraction of defatted walnut powder by ultrasonic assisted and artificical neural network
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Food Science and Technology (Campinas) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612022000100498 |
Resumo: | Abstract In order to obtain the extraction process of defatted walnut powder (DWP), an ultrasound-assisted extraction based on artificial neural network was established, and the activity of the extract was evaluated. The artificial neural network (ANN) was used to model different parameters, including the yield of extraction, the concentrations of glansreginin A and ellagic acid, and obtained the optimal extraction process: solvent to material ratio of 9.5 mL/g, ethanol concentration of 68%, extraction period of 55 min, and extraction three times. Then, the antioxidant scavenging ability of DWP obtained by ANN was compared with other extraction methods. The results showed that DWP extracted by artificial neural network demonstrated good activity in scavenging DPPH and ABTS radicals. |
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Food Science and Technology (Campinas) |
repository_id_str |
|
spelling |
Optimization of extraction of defatted walnut powder by ultrasonic assisted and artificical neural networkultrasound-assisted extractiondefatted walnut powderartificial neural networkantioxidant activityAbstract In order to obtain the extraction process of defatted walnut powder (DWP), an ultrasound-assisted extraction based on artificial neural network was established, and the activity of the extract was evaluated. The artificial neural network (ANN) was used to model different parameters, including the yield of extraction, the concentrations of glansreginin A and ellagic acid, and obtained the optimal extraction process: solvent to material ratio of 9.5 mL/g, ethanol concentration of 68%, extraction period of 55 min, and extraction three times. Then, the antioxidant scavenging ability of DWP obtained by ANN was compared with other extraction methods. The results showed that DWP extracted by artificial neural network demonstrated good activity in scavenging DPPH and ABTS radicals.Sociedade Brasileira de Ciência e Tecnologia de Alimentos2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612022000100498Food Science and Technology v.42 2022reponame:Food Science and Technology (Campinas)instname:Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA)instacron:SBCTA10.1590/fst.53320info:eu-repo/semantics/openAccessXU,XiajingREN,ShumengWANG,DongmeiMA,JingYAN,XiaoweiGUO,YongliLIU,XiaoqiuPAN,Yingnieng2022-02-23T00:00:00Zoai:scielo:S0101-20612022000100498Revistahttp://www.scielo.br/ctaONGhttps://old.scielo.br/oai/scielo-oai.php||revista@sbcta.org.br1678-457X0101-2061opendoar:2022-02-23T00:00Food Science and Technology (Campinas) - Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA)false |
dc.title.none.fl_str_mv |
Optimization of extraction of defatted walnut powder by ultrasonic assisted and artificical neural network |
title |
Optimization of extraction of defatted walnut powder by ultrasonic assisted and artificical neural network |
spellingShingle |
Optimization of extraction of defatted walnut powder by ultrasonic assisted and artificical neural network XU,Xiajing ultrasound-assisted extraction defatted walnut powder artificial neural network antioxidant activity |
title_short |
Optimization of extraction of defatted walnut powder by ultrasonic assisted and artificical neural network |
title_full |
Optimization of extraction of defatted walnut powder by ultrasonic assisted and artificical neural network |
title_fullStr |
Optimization of extraction of defatted walnut powder by ultrasonic assisted and artificical neural network |
title_full_unstemmed |
Optimization of extraction of defatted walnut powder by ultrasonic assisted and artificical neural network |
title_sort |
Optimization of extraction of defatted walnut powder by ultrasonic assisted and artificical neural network |
author |
XU,Xiajing |
author_facet |
XU,Xiajing REN,Shumeng WANG,Dongmei MA,Jing YAN,Xiaowei GUO,Yongli LIU,Xiaoqiu PAN,Yingni |
author_role |
author |
author2 |
REN,Shumeng WANG,Dongmei MA,Jing YAN,Xiaowei GUO,Yongli LIU,Xiaoqiu PAN,Yingni |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
XU,Xiajing REN,Shumeng WANG,Dongmei MA,Jing YAN,Xiaowei GUO,Yongli LIU,Xiaoqiu PAN,Yingni |
dc.subject.por.fl_str_mv |
ultrasound-assisted extraction defatted walnut powder artificial neural network antioxidant activity |
topic |
ultrasound-assisted extraction defatted walnut powder artificial neural network antioxidant activity |
description |
Abstract In order to obtain the extraction process of defatted walnut powder (DWP), an ultrasound-assisted extraction based on artificial neural network was established, and the activity of the extract was evaluated. The artificial neural network (ANN) was used to model different parameters, including the yield of extraction, the concentrations of glansreginin A and ellagic acid, and obtained the optimal extraction process: solvent to material ratio of 9.5 mL/g, ethanol concentration of 68%, extraction period of 55 min, and extraction three times. Then, the antioxidant scavenging ability of DWP obtained by ANN was compared with other extraction methods. The results showed that DWP extracted by artificial neural network demonstrated good activity in scavenging DPPH and ABTS radicals. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612022000100498 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612022000100498 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/fst.53320 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Ciência e Tecnologia de Alimentos |
publisher.none.fl_str_mv |
Sociedade Brasileira de Ciência e Tecnologia de Alimentos |
dc.source.none.fl_str_mv |
Food Science and Technology v.42 2022 reponame:Food Science and Technology (Campinas) instname:Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA) instacron:SBCTA |
instname_str |
Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA) |
instacron_str |
SBCTA |
institution |
SBCTA |
reponame_str |
Food Science and Technology (Campinas) |
collection |
Food Science and Technology (Campinas) |
repository.name.fl_str_mv |
Food Science and Technology (Campinas) - Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA) |
repository.mail.fl_str_mv |
||revista@sbcta.org.br |
_version_ |
1752126331610464256 |