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Optimization of extraction of bioactive compounds from piper corcovadensis C.DC leaves using a generalized linear model

2025-08-12Artigo científico Acesso aberto
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datacite.subject.fosCiências Naturais::Ciências Químicas
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
dc.contributor.authorFontoura, Bruno Henrique
dc.contributor.authorRamos, Luciano de Souza
dc.contributor.authorDallacorte, Lucas Vinícius
dc.contributor.authorRodrigues, Michelle Fernanda Faita
dc.contributor.authorMarchese, José Abramo
dc.contributor.authorFernandes, Tiago
dc.contributor.authorCunha, Mário Antônio Alves da
dc.contributor.authorLima, Vanderlei Aparecido de
dc.contributor.authorCarpes, Solange Teresinha
dc.date.accessioned2026-02-24T12:54:22Z
dc.date.available2026-02-24T12:54:22Z
dc.date.issued2025-08-12
dc.description.abstractThis concerns P. corcovadensis, an endemic plant of Brazil commonly used by the population due to its therapeutic properties. Optimizing chemical extraction conditions is critical for increasing the availability of bioactive compounds from plants. These compounds have antioxidant potential derived from a plant’s specialized metabolism and can exhibit a variety of biological actions. Therefore, statistical tools such as the Random Forest and Lazy KStar machine learning algorithms were used to determine the optimal condition for the extraction of phenolic compounds from P. corcovadensis leaves, with model evaluated by coefficient of determination (R2), mean square root of calibration error (RMSEC), and residual predictive deviation (RPD). The optimal extraction condition was obtained using a mixture of 80/20% (ethanol/water) at 70 °C for 120 min. For those extracts, there were 11.64 ± 0.04 mg GAE g-1 and antioxidant activity of 21.27 ± 0.53 mmol Trolox g-1, 33.15 ± 11.66 mmol Trolox g-1, and 13.47 ± 1.37 mmol Fe2+ by DPPH, ABTS and FRAP tests. With this study, we have shown that mathematical modelling can also be helpful in experimental sciences and can be used to develop predictive models. It was possible to develop predictive models for total phenolic compounds determination using the Random Forest and Lazy KStar machine learning algorithms. The Random Forest algorithm performed very well for DPPH modelling, giving us the confidence to use it to prediction antioxidant activity.eng
dc.description.sponsorshipThe authors gratefully acknowledge scholarship from the Brazilian National Research Council (CNPq), the Coordination for the Improvement of Higher-Level Personnel (CAPES), and Fundação Araucária. The authors also gratefully acknowledge the Foundation for Science and Technology (FCT) through the projects UIDB/00239/2020 [CEF], UIDP/00100/2020 [CQE] ( h t t p s : / / d o i . o r g / 1 0 . 5 4 4 9 9 / U I D P / 0 0 1 0 0 / 2 0 2 0), UIDB/00100/2020 [CQE] ( h t t p s : / / d o i . o r g / 1 0 . 5 4 4 9 9 / U I D B / 0 0 1 0 0 / 2 0 2 0), LA/P/0056/2020 ( h t t p s : / / d o i . o r g / 1 0 . 5 4 4 9 9 / L A / P / 0 0 5 6 / 2 0 2 0), contract CEECIND/02725/2018.
dc.identifier.citationBruno Henrique Fontoura, Luciano de Souza Ramos, Lucas Vinícius Dallacorte, Michelle Fernanda Faita Rodrigues, José Abramo Marchese, Tiago Adriano Fernandes, Mário Antônio Alves da Cunha, Vanderlei Aparecido de Lima, Solange Teresinha Carpes, Optimization of extraction of bioactive compounds from Piper corcovadensis C.DC leaves using a generalized linear model, Journal of Food Science and Technology, July 2025, https://doi.org/10.1007/s13197-025-06433-6
dc.identifier.doi10.1007/s13197-025-06433-6
dc.identifier.issn0975-8402
dc.identifier.urihttp://hdl.handle.net/10400.2/21487
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer
dc.relationForest Research Centre
dc.relationCentro de Química Estrutural
dc.relationInstitute of Molecular Sciences
dc.relationMultifunctional BioMOFs for increased antimicrobial efficiency and antibiofilm applications
dc.relation.hasversionhttps://link.springer.com/article/10.1007/s13197-025-06433-6#citeas
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectSpecialized metabolism
dc.subjectPhenolic compounds
dc.subjectAntioxidant activity
dc.subjectGeneralized linear model
dc.subjectMachine learning
dc.titleOptimization of extraction of bioactive compounds from piper corcovadensis C.DC leaves using a generalized linear modeleng
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleForest Research Centre
oaire.awardTitleCentro de Química Estrutural
oaire.awardTitleInstitute of Molecular Sciences
oaire.awardTitleMultifunctional BioMOFs for increased antimicrobial efficiency and antibiofilm applications
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00239%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F00100%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0056%2F2020/PT
oaire.awardURIhttp://hdl.handle.net/10400.2/19867
oaire.citation.titleJournal of Food Science and Technology
oaire.citation.volume2025
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStreamCEEC IND 2018
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameFernandes
person.givenNameTiago
person.identifier.ciencia-id8810-5C8A-08D0
person.identifier.orcid0000-0002-3374-612X
person.identifier.ridB-6777-2013
person.identifier.scopus-author-id24449123500
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
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