Monitoring of soluble pectin content in orange juice by means of MIR and TD-NMR spectroscopy combined with machine learning

Página do item simplificado
dc.contributor.authorBizzani, Marilia [UNESP]
dc.contributor.authorWilliam Menezes Flores, Douglas
dc.contributor.authorAlberto Colnago, Luiz
dc.contributor.authorDavid Ferreira, Marcos
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionEmpresa Brasileira de Pesquisa Agropecuária (EMBRAPA)
dc.date.accessioned2020-12-12T02:12:17Z
dc.date.available2020-12-12T02:12:17Z
dc.date.issued2020-12-01
dc.description.abstractThis study represents a rapid and non-destructive approach based on mid-infrared (MIR) spectroscopy, time domain nuclear magnetic resonance (TD-NMR), and machine learning classification models (ML) for monitoring soluble pectin content (SPC) changes in orange juice. Current reference methods of SPC in orange juice are laborious, requiring several extractions with successive adjustments hindering rapid process intervention. 109 fresh orange juices samples, representing different harvests, were analysed using MIR, TD-NMR and reference method. Unsupervised algorithms were applied for natural clustering of MIR and TD-NMR data in two groups. Analyses of variance of the two MIR and TD-NMR datasets show that only the MIR groups were different at 95% confidence for SPC average values. This approach allows build classification models based on MIR data achieving 85% and 89% of accuracy. Results demonstrate that MIR/ML can be a suitable strategy for the quick assessment of SPC trends in orange juices.en
dc.description.affiliationDepartment of Food and Nutrition Faculty of Pharmaceutical Sciences State University of São Paulo (UNESP), Rodovia Araraquara-Jaú, km 1
dc.description.affiliationDepartment of Agroindustry Food and Nutrition (LAN) “Luiz de Queiroz” School of Agriculture University of São Paulo, Avenida Pádua Dias 11
dc.description.affiliationEmbrapa Instrumentation, Rua XV de Novembro 1452
dc.description.affiliationUnespDepartment of Food and Nutrition Faculty of Pharmaceutical Sciences State University of São Paulo (UNESP), Rodovia Araraquara-Jaú, km 1
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipIdFAPESP: 13/23479-0
dc.description.sponsorshipIdFAPESP: 2019/13656-8
dc.description.sponsorshipIdCNPq: 303837-2013-6
dc.description.sponsorshipIdCNPq: 403075/2013-0
dc.identifierhttp://dx.doi.org/10.1016/j.foodchem.2020.127383
dc.identifier.citationFood Chemistry, v. 332.
dc.identifier.doi10.1016/j.foodchem.2020.127383
dc.identifier.issn1873-7072
dc.identifier.issn0308-8146
dc.identifier.scopus2-s2.0-85086990753
dc.identifier.urihttp://hdl.handle.net/11449/200646
dc.language.isoeng
dc.relation.ispartofFood Chemistry
dc.sourceScopus
dc.subjectData science
dc.subjectMachine learning
dc.subjectMIR
dc.subjectOrange juice
dc.subjectSoluble pectin content (SPC)
dc.subjectTD-NMR
dc.titleMonitoring of soluble pectin content in orange juice by means of MIR and TD-NMR spectroscopy combined with machine learningen
dc.typeArtigo
unesp.author.orcid0000-0001-7018-5870[1]

Arquivos

Coleções

Artigos - Alimentos e Nutrição - FCFAR