Monitoring of soluble pectin content in orange juice by means of MIR and TD-NMR spectroscopy combined with machine learning
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.foodchem.2020.127383 http://hdl.handle.net/11449/200646 |
Resumo: | This 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. |
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Monitoring of soluble pectin content in orange juice by means of MIR and TD-NMR spectroscopy combined with machine learningData scienceMachine learningMIROrange juiceSoluble pectin content (SPC)TD-NMRThis 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.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Department of Food and Nutrition Faculty of Pharmaceutical Sciences State University of São Paulo (UNESP), Rodovia Araraquara-Jaú, km 1Department of Agroindustry Food and Nutrition (LAN) “Luiz de Queiroz” School of Agriculture University of São Paulo, Avenida Pádua Dias 11Embrapa Instrumentation, Rua XV de Novembro 1452Department of Food and Nutrition Faculty of Pharmaceutical Sciences State University of São Paulo (UNESP), Rodovia Araraquara-Jaú, km 1FAPESP: 13/23479-0FAPESP: 2019/13656-8CNPq: 303837-2013-6CNPq: 403075/2013-0Universidade Estadual Paulista (Unesp)Universidade de São Paulo (USP)Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA)Bizzani, Marilia [UNESP]William Menezes Flores, DouglasAlberto Colnago, LuizDavid Ferreira, Marcos2020-12-12T02:12:17Z2020-12-12T02:12:17Z2020-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.foodchem.2020.127383Food Chemistry, v. 332.1873-70720308-8146http://hdl.handle.net/11449/20064610.1016/j.foodchem.2020.1273832-s2.0-85086990753Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengFood Chemistryinfo:eu-repo/semantics/openAccess2024-06-21T12:47:24Zoai:repositorio.unesp.br:11449/200646Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T23:52:37.181697Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Monitoring of soluble pectin content in orange juice by means of MIR and TD-NMR spectroscopy combined with machine learning |
title |
Monitoring of soluble pectin content in orange juice by means of MIR and TD-NMR spectroscopy combined with machine learning |
spellingShingle |
Monitoring of soluble pectin content in orange juice by means of MIR and TD-NMR spectroscopy combined with machine learning Bizzani, Marilia [UNESP] Data science Machine learning MIR Orange juice Soluble pectin content (SPC) TD-NMR |
title_short |
Monitoring of soluble pectin content in orange juice by means of MIR and TD-NMR spectroscopy combined with machine learning |
title_full |
Monitoring of soluble pectin content in orange juice by means of MIR and TD-NMR spectroscopy combined with machine learning |
title_fullStr |
Monitoring of soluble pectin content in orange juice by means of MIR and TD-NMR spectroscopy combined with machine learning |
title_full_unstemmed |
Monitoring of soluble pectin content in orange juice by means of MIR and TD-NMR spectroscopy combined with machine learning |
title_sort |
Monitoring of soluble pectin content in orange juice by means of MIR and TD-NMR spectroscopy combined with machine learning |
author |
Bizzani, Marilia [UNESP] |
author_facet |
Bizzani, Marilia [UNESP] William Menezes Flores, Douglas Alberto Colnago, Luiz David Ferreira, Marcos |
author_role |
author |
author2 |
William Menezes Flores, Douglas Alberto Colnago, Luiz David Ferreira, Marcos |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Universidade de São Paulo (USP) Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA) |
dc.contributor.author.fl_str_mv |
Bizzani, Marilia [UNESP] William Menezes Flores, Douglas Alberto Colnago, Luiz David Ferreira, Marcos |
dc.subject.por.fl_str_mv |
Data science Machine learning MIR Orange juice Soluble pectin content (SPC) TD-NMR |
topic |
Data science Machine learning MIR Orange juice Soluble pectin content (SPC) TD-NMR |
description |
This 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. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-12-12T02:12:17Z 2020-12-12T02:12:17Z 2020-12-01 |
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.foodchem.2020.127383 Food Chemistry, v. 332. 1873-7072 0308-8146 http://hdl.handle.net/11449/200646 10.1016/j.foodchem.2020.127383 2-s2.0-85086990753 |
url |
http://dx.doi.org/10.1016/j.foodchem.2020.127383 http://hdl.handle.net/11449/200646 |
identifier_str_mv |
Food Chemistry, v. 332. 1873-7072 0308-8146 10.1016/j.foodchem.2020.127383 2-s2.0-85086990753 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Food Chemistry |
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_ |
1808129561399918592 |