Selection of industrial tomatoes using TD-NMR data and computational classification methods

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorEmpresa Brasileira de Pesquisa Agropecuária (EMBRAPA)-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.creatorBorba, Karla R. [UNESP]-
Autor(es): dc.creatorOldoni, Fernanda C.A. [UNESP]-
Autor(es): dc.creatorMonaretto, Tatiana-
Autor(es): dc.creatorColnago, Luiz A.-
Autor(es): dc.creatorFerreira, Marcos D.-
Data de aceite: dc.date.accessioned2022-02-22T00:49:55Z-
Data de disponibilização: dc.date.available2022-02-22T00:49:55Z-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-05-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.microc.2021.106048-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/207334-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/207334-
Descrição: dc.descriptionTomato processing chain has a world economic relevance for the food industry and the agribusiness, providing ready-to-eat products and raw material for other production chains. The product quality is depending on control of some fruit attributes, such as color, soluble solids content (SSC), and defects. The aim of this study was to develop accurate and nondestructive classification models according to the tomato maturation stage, SSC, and presence of defects using Time-Domain Nuclear Magnetic Resonance (TD-NMR) associated with computational classification methods. Each class showed different decay times. Green tomatoes showed a shorter decay signal than red tomatoes, mainly due to the relaxation signal being related to the water mobility in different vegetable tissue compartments. Classification models resulted in great accuracy performances, the best accuracy for each classification were: maturity index: 97% (SVM); SSC: 100% (SVM and kNN); presence of defects: 90% (PLS-DA). These results show that CPMG decays associated with computational methods can be used in the tomato processing industry to classify tomato samples. These classification models showed the potential of TD-NMR technique in a high-throughput screening application before the processing.-
Descrição: dc.descriptionDepartment of Food and Nutrition School of Pharmaceutical Sciences São Paulo State University-UNESP, Araraquara – Jaú, Km 1-
Descrição: dc.descriptionEmbrapa Instrumentation, XV de Novembro, 1452-
Descrição: dc.descriptionSão Carlos Institute of Chemistry São Paulo Universtity, Trabalhador São Carlense Avenue 400-
Descrição: dc.descriptionDepartment of Food and Nutrition School of Pharmaceutical Sciences São Paulo State University-UNESP, Araraquara – Jaú, Km 1-
Idioma: dc.languageen-
Relação: dc.relationMicrochemical Journal-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectChemometrics-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectNMR-
Palavras-chave: dc.subjectRelaxation time-
Palavras-chave: dc.subjectTomato processing-
Título: dc.titleSelection of industrial tomatoes using TD-NMR data and computational classification methods-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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