Selection of industrial tomatoes using TD-NMR data and computational classification methods
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.microc.2021.106048 http://hdl.handle.net/11449/207334 |
Resumo: | Tomato 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. |
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Selection of industrial tomatoes using TD-NMR data and computational classification methodsChemometricsMachine learningNMRRelaxation timeTomato processingTomato 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.Department of Food and Nutrition School of Pharmaceutical Sciences São Paulo State University-UNESP, Araraquara – Jaú, Km 1Embrapa Instrumentation, XV de Novembro, 1452São Carlos Institute of Chemistry São Paulo Universtity, Trabalhador São Carlense Avenue 400Department of Food and Nutrition School of Pharmaceutical Sciences São Paulo State University-UNESP, Araraquara – Jaú, Km 1Universidade Estadual Paulista (Unesp)Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA)Universidade de São Paulo (USP)Borba, Karla R. [UNESP]Oldoni, Fernanda C.A. [UNESP]Monaretto, TatianaColnago, Luiz A.Ferreira, Marcos D.2021-06-25T10:53:24Z2021-06-25T10:53:24Z2021-05-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.microc.2021.106048Microchemical Journal, v. 164.0026-265Xhttp://hdl.handle.net/11449/20733410.1016/j.microc.2021.1060482-s2.0-85101381000Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengMicrochemical Journalinfo:eu-repo/semantics/openAccess2024-06-21T12:46:49Zoai:repositorio.unesp.br:11449/207334Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:36:39.148410Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Selection of industrial tomatoes using TD-NMR data and computational classification methods |
title |
Selection of industrial tomatoes using TD-NMR data and computational classification methods |
spellingShingle |
Selection of industrial tomatoes using TD-NMR data and computational classification methods Borba, Karla R. [UNESP] Chemometrics Machine learning NMR Relaxation time Tomato processing |
title_short |
Selection of industrial tomatoes using TD-NMR data and computational classification methods |
title_full |
Selection of industrial tomatoes using TD-NMR data and computational classification methods |
title_fullStr |
Selection of industrial tomatoes using TD-NMR data and computational classification methods |
title_full_unstemmed |
Selection of industrial tomatoes using TD-NMR data and computational classification methods |
title_sort |
Selection of industrial tomatoes using TD-NMR data and computational classification methods |
author |
Borba, Karla R. [UNESP] |
author_facet |
Borba, Karla R. [UNESP] Oldoni, Fernanda C.A. [UNESP] Monaretto, Tatiana Colnago, Luiz A. Ferreira, Marcos D. |
author_role |
author |
author2 |
Oldoni, Fernanda C.A. [UNESP] Monaretto, Tatiana Colnago, Luiz A. Ferreira, Marcos D. |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA) Universidade de São Paulo (USP) |
dc.contributor.author.fl_str_mv |
Borba, Karla R. [UNESP] Oldoni, Fernanda C.A. [UNESP] Monaretto, Tatiana Colnago, Luiz A. Ferreira, Marcos D. |
dc.subject.por.fl_str_mv |
Chemometrics Machine learning NMR Relaxation time Tomato processing |
topic |
Chemometrics Machine learning NMR Relaxation time Tomato processing |
description |
Tomato 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. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-06-25T10:53:24Z 2021-06-25T10:53:24Z 2021-05-01 |
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://dx.doi.org/10.1016/j.microc.2021.106048 Microchemical Journal, v. 164. 0026-265X http://hdl.handle.net/11449/207334 10.1016/j.microc.2021.106048 2-s2.0-85101381000 |
url |
http://dx.doi.org/10.1016/j.microc.2021.106048 http://hdl.handle.net/11449/207334 |
identifier_str_mv |
Microchemical Journal, v. 164. 0026-265X 10.1016/j.microc.2021.106048 2-s2.0-85101381000 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Microchemical Journal |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128677358075904 |