Optimisation of an enzymatic method to obtain modified artichoke pectin and pectic oligosaccharides using artificial neural network tools. In silico and in vitro assessment of the antioxidant activity

An enzymatic procedure to obtain modified artichoke pectin and pectic oligosaccharides (POS) (Mw 100–0.3 kDa) has been optimised through an experimental design analysed by artificial neural networks (ANN; R2 0.99), leading to high yields of these products (65.9 ± 2.1 mg 100 mg−1 pectin) at optimal c...

Descripción completa

Detalles Bibliográficos
Autores: Sabater, Carlos, Blanco-Doval, Ana, Montilla, Antonia, Corzo, Nieves
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2021
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/250672
Acceso en línea:http://hdl.handle.net/10261/250672
Access Level:acceso abierto
id ES_a135d5caade435868ab51169d0e05117
oai_identifier_str oai:digital.csic.es:10261/250672
network_acronym_str ES
network_name_str España
repository_id_str
spelling Optimisation of an enzymatic method to obtain modified artichoke pectin and pectic oligosaccharides using artificial neural network tools. In silico and in vitro assessment of the antioxidant activitySabater, CarlosBlanco-Doval, AnaMontilla, AntoniaCorzo, NievesAn enzymatic procedure to obtain modified artichoke pectin and pectic oligosaccharides (POS) (Mw 100–0.3 kDa) has been optimised through an experimental design analysed by artificial neural networks (ANN; R2 0.99), leading to high yields of these products (65.9 ± 2.1 mg 100 mg−1 pectin) at optimal conditions (pH 4.41, reaction time 0.9 h, enzyme dose 17.1 U g−1 pectin), reaching a maximum theoretical desirability of 0.98. Desirability function, variable importance and sensitivity analysis were performed to interpret ANN while residual analysis demonstrated its high predictive power. Hydrolysates were purified by ultrafiltration and retentate and permeate fractions were characterised by MALDI-TOF-MS. Oligosaccharides from di- to hexasaccharides corresponding to galacturonic acid (GalA) oligomers that may be attached to neutral sugars and ferulic acid were determined, and their potential free radical scavenger activity was calculated using an in silico model (72–98% probability). The presence of specific structures in permeate (high free GalA content, GalA oligomers attached to xylose, ferulic acid or rhamnose and arabinose) and retentate fractions explained differences observed in their in vitro antioxidant activities (135.6 and 32.1 μmol Trolox g−1, respectively). The combination of in silico and in vitro methods allows establishing structure-activity relationships for modified pectin and POS fractions.This work has been funded by MICINN of Spain, Projects AGL2014-53445-R and AGL2017-84614-C2-1-R. Carlos Sabater thanks his FPU Predoc contract from Spanish MECD (FPU14/03619).Peer reviewedElsevierMinisterio de Ciencia, Innovación y Universidades (España)Agencia Estatal de Investigación (España)Ministerio de Economía y Competitividad (España)Ministerio de Educación, Cultura y Deporte (España)Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202120212021info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Postprintinfo:eu-repo/semantics/acceptedVersionhttp://hdl.handle.net/10261/250672reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/AGL2014-53445-Rinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/AGL2017-84614-C2-1-Rhttps://doi.org/10.1016/j.foodhyd.2020.106161Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2506722026-05-22T06:33:51Z
dc.title.none.fl_str_mv Optimisation of an enzymatic method to obtain modified artichoke pectin and pectic oligosaccharides using artificial neural network tools. In silico and in vitro assessment of the antioxidant activity
title Optimisation of an enzymatic method to obtain modified artichoke pectin and pectic oligosaccharides using artificial neural network tools. In silico and in vitro assessment of the antioxidant activity
spellingShingle Optimisation of an enzymatic method to obtain modified artichoke pectin and pectic oligosaccharides using artificial neural network tools. In silico and in vitro assessment of the antioxidant activity
Sabater, Carlos
title_short Optimisation of an enzymatic method to obtain modified artichoke pectin and pectic oligosaccharides using artificial neural network tools. In silico and in vitro assessment of the antioxidant activity
title_full Optimisation of an enzymatic method to obtain modified artichoke pectin and pectic oligosaccharides using artificial neural network tools. In silico and in vitro assessment of the antioxidant activity
title_fullStr Optimisation of an enzymatic method to obtain modified artichoke pectin and pectic oligosaccharides using artificial neural network tools. In silico and in vitro assessment of the antioxidant activity
title_full_unstemmed Optimisation of an enzymatic method to obtain modified artichoke pectin and pectic oligosaccharides using artificial neural network tools. In silico and in vitro assessment of the antioxidant activity
title_sort Optimisation of an enzymatic method to obtain modified artichoke pectin and pectic oligosaccharides using artificial neural network tools. In silico and in vitro assessment of the antioxidant activity
dc.creator.none.fl_str_mv Sabater, Carlos
Blanco-Doval, Ana
Montilla, Antonia
Corzo, Nieves
author Sabater, Carlos
author_facet Sabater, Carlos
Blanco-Doval, Ana
Montilla, Antonia
Corzo, Nieves
author_role author
author2 Blanco-Doval, Ana
Montilla, Antonia
Corzo, Nieves
author2_role author
author
author
dc.contributor.none.fl_str_mv Ministerio de Ciencia, Innovación y Universidades (España)
Agencia Estatal de Investigación (España)
Ministerio de Economía y Competitividad (España)
Ministerio de Educación, Cultura y Deporte (España)
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
description An enzymatic procedure to obtain modified artichoke pectin and pectic oligosaccharides (POS) (Mw 100–0.3 kDa) has been optimised through an experimental design analysed by artificial neural networks (ANN; R2 0.99), leading to high yields of these products (65.9 ± 2.1 mg 100 mg−1 pectin) at optimal conditions (pH 4.41, reaction time 0.9 h, enzyme dose 17.1 U g−1 pectin), reaching a maximum theoretical desirability of 0.98. Desirability function, variable importance and sensitivity analysis were performed to interpret ANN while residual analysis demonstrated its high predictive power. Hydrolysates were purified by ultrafiltration and retentate and permeate fractions were characterised by MALDI-TOF-MS. Oligosaccharides from di- to hexasaccharides corresponding to galacturonic acid (GalA) oligomers that may be attached to neutral sugars and ferulic acid were determined, and their potential free radical scavenger activity was calculated using an in silico model (72–98% probability). The presence of specific structures in permeate (high free GalA content, GalA oligomers attached to xylose, ferulic acid or rhamnose and arabinose) and retentate fractions explained differences observed in their in vitro antioxidant activities (135.6 and 32.1 μmol Trolox g−1, respectively). The combination of in silico and in vitro methods allows establishing structure-activity relationships for modified pectin and POS fractions.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021
2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Postprint
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/250672
url http://hdl.handle.net/10261/250672
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/AGL2014-53445-R
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/AGL2017-84614-C2-1-R
https://doi.org/10.1016/j.foodhyd.2020.106161
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869415125740421121
score 15,811543

Similares en LA Referencia