Extraction of phenolic compounds from cocoa shell: modeling using response surface methodology and artificial neural networks

This work's objective was to model and optimize a green extraction method of phenolic compounds from the cocoa shell as a strategy to revalorize this by-product, obtaining novel high-value products. According to a Box-Behnken design, 27 extractions were carried out at different conditions of te...

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Autores: Rebollo Hernanz, Miguel, Cañas Rodríguez, Silvia, Taladrid Gandía, Diego, Segovia, Ángela, Bartolomé, Begoña, Aguilera Gutiérrez, Yolanda, Martín Cabrejas, M. Ángeles
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universidad Autónoma de Madrid
Repositorio:Biblos-e Archivo. Repositorio Institucional de la UAM
Idioma:inglés
OAI Identifier:oai:repositorio.uam.es:10486/700515
Acceso en línea:http://hdl.handle.net/10486/700515
https://dx.doi.org/10.1016/j.seppur.2021.118779
Access Level:acceso abierto
Palabra clave:Antioxidant capacity
Artificial neural networks
Cocoa by-products
Green extraction
Phenolic compounds
Response surface methodology
Química
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spelling Extraction of phenolic compounds from cocoa shell: modeling using response surface methodology and artificial neural networksRebollo Hernanz, MiguelCañas Rodríguez, SilviaTaladrid Gandía, DiegoSegovia, ÁngelaBartolomé, BegoñaAguilera Gutiérrez, YolandaMartín Cabrejas, M. ÁngelesAntioxidant capacityArtificial neural networksCocoa by-productsGreen extractionPhenolic compoundsResponse surface methodologyQuímicaThis work's objective was to model and optimize a green extraction method of phenolic compounds from the cocoa shell as a strategy to revalorize this by-product, obtaining novel high-value products. According to a Box-Behnken design, 27 extractions were carried out at different conditions of temperature, time, acidity, and solid-to-liquid ratio. Total phenolic compounds, flavonoids, flavanols, proanthocyanidins, phenolic acids, o-diphenols, and in vitro antioxidant capacity were assessed in each extract. Response surface methodology (RSM) and artificial neural networks (ANN) were used to model the effect of the different parameters on the green aqueous extraction of phenolic compounds from the cocoa shell. The obtained mathematical models fitted well for all the responses. RSM and ANN exhibited high estimation capabilities. The main factors affecting phenolic extraction were temperature, followed by solid-to-liquid ratio, and acidity. The optimal extraction conditions were 100 °C, 90 min, 0% citric acid, and 0.02 g cocoa shell mL−1 water. Under these conditions, experimental values for the response variables matched those predicted, therefore, validating the model. UPLC-ESI-MS/MS revealed the presence of 15 phenolic compounds, being protocatechuic acid, procyanidin B2, (−)-epicatechin, and (+)-catechin, the major ones. Spectrophotometric results showed a significant correlation with the UPLC results, confirming their potential use for screening and optimization purposes. Aqueous phenolic extracts from the cocoa shell would have potential use as sustainable food-grade ingredients and nutraceutical productsThis work was supported by UAM-Santander (grant number 2017/ EEUU/01) and COCARDIOLAC (grant number RTI2018-097504-B-I00) projects, and Community of Madrid and UAM Agreement (2019–2023). M. Rebollo-Hernanz thanks to the FPU program of the Ministry of Universities for his predoctoral fellowship (grant number FPU15/04238)ElsevierDepartamento de Química OrgánicaFacultad de CienciasUAM. Departamento de Química Agrícola20212021-04-19research articlehttp://purl.org/coar/resource_type/c_2df8fbb1VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10486/700515https://dx.doi.org/10.1016/j.seppur.2021.118779reponame:Biblos-e Archivo. Repositorio Institucional de la UAMinstname:Universidad Autónoma de MadridInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:repositorio.uam.es:10486/7005152026-06-23T12:46:27Z
dc.title.none.fl_str_mv Extraction of phenolic compounds from cocoa shell: modeling using response surface methodology and artificial neural networks
title Extraction of phenolic compounds from cocoa shell: modeling using response surface methodology and artificial neural networks
spellingShingle Extraction of phenolic compounds from cocoa shell: modeling using response surface methodology and artificial neural networks
Rebollo Hernanz, Miguel
Antioxidant capacity
Artificial neural networks
Cocoa by-products
Green extraction
Phenolic compounds
Response surface methodology
Química
title_short Extraction of phenolic compounds from cocoa shell: modeling using response surface methodology and artificial neural networks
title_full Extraction of phenolic compounds from cocoa shell: modeling using response surface methodology and artificial neural networks
title_fullStr Extraction of phenolic compounds from cocoa shell: modeling using response surface methodology and artificial neural networks
title_full_unstemmed Extraction of phenolic compounds from cocoa shell: modeling using response surface methodology and artificial neural networks
title_sort Extraction of phenolic compounds from cocoa shell: modeling using response surface methodology and artificial neural networks
dc.creator.none.fl_str_mv Rebollo Hernanz, Miguel
Cañas Rodríguez, Silvia
Taladrid Gandía, Diego
Segovia, Ángela
Bartolomé, Begoña
Aguilera Gutiérrez, Yolanda
Martín Cabrejas, M. Ángeles
author Rebollo Hernanz, Miguel
author_facet Rebollo Hernanz, Miguel
Cañas Rodríguez, Silvia
Taladrid Gandía, Diego
Segovia, Ángela
Bartolomé, Begoña
Aguilera Gutiérrez, Yolanda
Martín Cabrejas, M. Ángeles
author_role author
author2 Cañas Rodríguez, Silvia
Taladrid Gandía, Diego
Segovia, Ángela
Bartolomé, Begoña
Aguilera Gutiérrez, Yolanda
Martín Cabrejas, M. Ángeles
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Departamento de Química Orgánica
Facultad de Ciencias
UAM. Departamento de Química Agrícola
dc.subject.none.fl_str_mv Antioxidant capacity
Artificial neural networks
Cocoa by-products
Green extraction
Phenolic compounds
Response surface methodology
Química
topic Antioxidant capacity
Artificial neural networks
Cocoa by-products
Green extraction
Phenolic compounds
Response surface methodology
Química
description This work's objective was to model and optimize a green extraction method of phenolic compounds from the cocoa shell as a strategy to revalorize this by-product, obtaining novel high-value products. According to a Box-Behnken design, 27 extractions were carried out at different conditions of temperature, time, acidity, and solid-to-liquid ratio. Total phenolic compounds, flavonoids, flavanols, proanthocyanidins, phenolic acids, o-diphenols, and in vitro antioxidant capacity were assessed in each extract. Response surface methodology (RSM) and artificial neural networks (ANN) were used to model the effect of the different parameters on the green aqueous extraction of phenolic compounds from the cocoa shell. The obtained mathematical models fitted well for all the responses. RSM and ANN exhibited high estimation capabilities. The main factors affecting phenolic extraction were temperature, followed by solid-to-liquid ratio, and acidity. The optimal extraction conditions were 100 °C, 90 min, 0% citric acid, and 0.02 g cocoa shell mL−1 water. Under these conditions, experimental values for the response variables matched those predicted, therefore, validating the model. UPLC-ESI-MS/MS revealed the presence of 15 phenolic compounds, being protocatechuic acid, procyanidin B2, (−)-epicatechin, and (+)-catechin, the major ones. Spectrophotometric results showed a significant correlation with the UPLC results, confirming their potential use for screening and optimization purposes. Aqueous phenolic extracts from the cocoa shell would have potential use as sustainable food-grade ingredients and nutraceutical products
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-04-19
dc.type.none.fl_str_mv research article
http://purl.org/coar/resource_type/c_2df8fbb1
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10486/700515
https://dx.doi.org/10.1016/j.seppur.2021.118779
url http://hdl.handle.net/10486/700515
https://dx.doi.org/10.1016/j.seppur.2021.118779
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Biblos-e Archivo. Repositorio Institucional de la UAM
instname:Universidad Autónoma de Madrid
instname_str Universidad Autónoma de Madrid
reponame_str Biblos-e Archivo. Repositorio Institucional de la UAM
collection Biblos-e Archivo. Repositorio Institucional de la UAM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300719

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