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

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorEmpresa Brasileira de Pesquisa Agropecuária (EMBRAPA)-
Autor(es): dc.creatorBizzani, Marilia [UNESP]-
Autor(es): dc.creatorWilliam Menezes Flores, Douglas-
Autor(es): dc.creatorAlberto Colnago, Luiz-
Autor(es): dc.creatorDavid Ferreira, Marcos-
Data de aceite: dc.date.accessioned2022-02-22T00:31:18Z-
Data de disponibilização: dc.date.available2022-02-22T00:31:18Z-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-11-30-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.foodchem.2020.127383-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/200646-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/200646-
Descrição: dc.descriptionThis 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.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionDepartment of Food and Nutrition Faculty of Pharmaceutical Sciences State University of São Paulo (UNESP), Rodovia Araraquara-Jaú, km 1-
Descrição: dc.descriptionDepartment of Agroindustry Food and Nutrition (LAN) “Luiz de Queiroz” School of Agriculture University of São Paulo, Avenida Pádua Dias 11-
Descrição: dc.descriptionEmbrapa Instrumentation, Rua XV de Novembro 1452-
Descrição: dc.descriptionDepartment of Food and Nutrition Faculty of Pharmaceutical Sciences State University of São Paulo (UNESP), Rodovia Araraquara-Jaú, km 1-
Descrição: dc.descriptionFAPESP: 13/23479-0-
Descrição: dc.descriptionFAPESP: 2019/13656-8-
Descrição: dc.descriptionCNPq: 303837-2013-6-
Descrição: dc.descriptionCNPq: 403075/2013-0-
Idioma: dc.languageen-
Relação: dc.relationFood Chemistry-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectData science-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectMIR-
Palavras-chave: dc.subjectOrange juice-
Palavras-chave: dc.subjectSoluble pectin content (SPC)-
Palavras-chave: dc.subjectTD-NMR-
Título: dc.titleMonitoring of soluble pectin content in orange juice by means of MIR and TD-NMR spectroscopy combined with machine learning-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

Não existem arquivos associados a este item.