Response surface methodology and artificial neural network modeling as predictive tools for phenolic compounds recovery from olive pomace
Autor(a) principal: | |
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Data de Publicação: | 2024 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10198/29184 |
Resumo: | This study optimized the extraction of three major phenolic compounds (oleuropein, tyrosol, and verbascoside) from olive pomace using microwave- and ultrasonic-assisted methods. Screening factorial design (SFD) and central composite design (CCD) were employed, and response surface methodology (RSM) and artificial neural networks (ANN) were used for data modeling. The microwave-assisted method in the SFD yielded higher compound amounts, with verbascoside showing a four-fold increase compared to the ultrasonic-assisted method. Factors like vessel diameter, ultrasonic power using UAE, and solvent acidity in both techniques had minimally impacted extractability. CCD-RSM revealed temperaturés significantly affect on oleuropein, but improved tyrosol recovery, with the effect on verbascoside being influenced by the temperature range. RSM and ANN integration enhanced understanding and prediction of factor behavior. Microwave-assisted extraction at 113 ◦C for 26 min, with minimum ramp time of 7.7 min, yielded 67.4, 57, and 5.1 mg of oleuropein, tyrosol, and verbascoside per gram of extract, respectively, with a prediction error ranging from 0.83 to 15.19. |
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Response surface methodology and artificial neural network modeling as predictive tools for phenolic compounds recovery from olive pomaceOlive pomacePhenolic compoundsDesign of experimentsResponse surface methodologyArtificial neural networksThis study optimized the extraction of three major phenolic compounds (oleuropein, tyrosol, and verbascoside) from olive pomace using microwave- and ultrasonic-assisted methods. Screening factorial design (SFD) and central composite design (CCD) were employed, and response surface methodology (RSM) and artificial neural networks (ANN) were used for data modeling. The microwave-assisted method in the SFD yielded higher compound amounts, with verbascoside showing a four-fold increase compared to the ultrasonic-assisted method. Factors like vessel diameter, ultrasonic power using UAE, and solvent acidity in both techniques had minimally impacted extractability. CCD-RSM revealed temperaturés significantly affect on oleuropein, but improved tyrosol recovery, with the effect on verbascoside being influenced by the temperature range. RSM and ANN integration enhanced understanding and prediction of factor behavior. Microwave-assisted extraction at 113 ◦C for 26 min, with minimum ramp time of 7.7 min, yielded 67.4, 57, and 5.1 mg of oleuropein, tyrosol, and verbascoside per gram of extract, respectively, with a prediction error ranging from 0.83 to 15.19.The authors are grateful to the Foundation for Science and Technology (FCT) for financial support to CIMO (UIDB/00690/2020 and UIDP/00690/2020), SusTEC (LA/P/0007/2020), L. Barros institutional contract, and Ana Rita Silva Doctoral Grant (SFRH/BD/145834/2019). To the ERDF through the Regional Operational Program North 2020, within the scope of the project OliveBIOextract (NORTE-01-0247- FEDER-049865). B. Melgar thanks the ERDF through the Regional Operational Program North 2020 for his contract within the Project OleaChain (NORTE-06-3559-FSE-000188). To MICINN for supporting the JDC contract of T. Oludemi (FJC2019-042549-I). Manuel Ayuso thanks PRIMA and FEDER-Interreg Espana- Portugal programme for financial support through the Local-NutLeg project (Section 1 2020 Agrofood Value Chain topic 1.3.1ElsevierBiblioteca Digital do IPBSilva, Ana RitaAyuso, ManuelOludemi, TaofiqGonçalves, AlexandreMelgar, BrunoBarros, Lillian2024-01-15T11:58:35Z20242024-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10198/29184engSilva, Ana Rita; Ayuso, Manuel; Oludemi, Taofiq; Gonçalves, Alexandre; Melgar, Bruno; Barros, Lillian (2024). Response surface methodology and artificial neural network modeling as predictive tools for phenolic compounds recovery from olive pomace. Separation and Purification Technology. ISSN 1383-5866. 330, p. 1-91383-586610.1016/j.seppur.2023.1253511873-3794info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-01-17T01:22:17Zoai:bibliotecadigital.ipb.pt:10198/29184Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:44:51.061155Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Response surface methodology and artificial neural network modeling as predictive tools for phenolic compounds recovery from olive pomace |
title |
Response surface methodology and artificial neural network modeling as predictive tools for phenolic compounds recovery from olive pomace |
spellingShingle |
Response surface methodology and artificial neural network modeling as predictive tools for phenolic compounds recovery from olive pomace Silva, Ana Rita Olive pomace Phenolic compounds Design of experiments Response surface methodology Artificial neural networks |
title_short |
Response surface methodology and artificial neural network modeling as predictive tools for phenolic compounds recovery from olive pomace |
title_full |
Response surface methodology and artificial neural network modeling as predictive tools for phenolic compounds recovery from olive pomace |
title_fullStr |
Response surface methodology and artificial neural network modeling as predictive tools for phenolic compounds recovery from olive pomace |
title_full_unstemmed |
Response surface methodology and artificial neural network modeling as predictive tools for phenolic compounds recovery from olive pomace |
title_sort |
Response surface methodology and artificial neural network modeling as predictive tools for phenolic compounds recovery from olive pomace |
author |
Silva, Ana Rita |
author_facet |
Silva, Ana Rita Ayuso, Manuel Oludemi, Taofiq Gonçalves, Alexandre Melgar, Bruno Barros, Lillian |
author_role |
author |
author2 |
Ayuso, Manuel Oludemi, Taofiq Gonçalves, Alexandre Melgar, Bruno Barros, Lillian |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Biblioteca Digital do IPB |
dc.contributor.author.fl_str_mv |
Silva, Ana Rita Ayuso, Manuel Oludemi, Taofiq Gonçalves, Alexandre Melgar, Bruno Barros, Lillian |
dc.subject.por.fl_str_mv |
Olive pomace Phenolic compounds Design of experiments Response surface methodology Artificial neural networks |
topic |
Olive pomace Phenolic compounds Design of experiments Response surface methodology Artificial neural networks |
description |
This study optimized the extraction of three major phenolic compounds (oleuropein, tyrosol, and verbascoside) from olive pomace using microwave- and ultrasonic-assisted methods. Screening factorial design (SFD) and central composite design (CCD) were employed, and response surface methodology (RSM) and artificial neural networks (ANN) were used for data modeling. The microwave-assisted method in the SFD yielded higher compound amounts, with verbascoside showing a four-fold increase compared to the ultrasonic-assisted method. Factors like vessel diameter, ultrasonic power using UAE, and solvent acidity in both techniques had minimally impacted extractability. CCD-RSM revealed temperaturés significantly affect on oleuropein, but improved tyrosol recovery, with the effect on verbascoside being influenced by the temperature range. RSM and ANN integration enhanced understanding and prediction of factor behavior. Microwave-assisted extraction at 113 ◦C for 26 min, with minimum ramp time of 7.7 min, yielded 67.4, 57, and 5.1 mg of oleuropein, tyrosol, and verbascoside per gram of extract, respectively, with a prediction error ranging from 0.83 to 15.19. |
publishDate |
2024 |
dc.date.none.fl_str_mv |
2024-01-15T11:58:35Z 2024 2024-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10198/29184 |
url |
http://hdl.handle.net/10198/29184 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Silva, Ana Rita; Ayuso, Manuel; Oludemi, Taofiq; Gonçalves, Alexandre; Melgar, Bruno; Barros, Lillian (2024). Response surface methodology and artificial neural network modeling as predictive tools for phenolic compounds recovery from olive pomace. Separation and Purification Technology. ISSN 1383-5866. 330, p. 1-9 1383-5866 10.1016/j.seppur.2023.125351 1873-3794 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
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:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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