Postharvest behavior of mangoes nondestructively sorted based on dry matter content during and after storage under controlled atmosphere
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.17660/th2019/74.6.4 http://hdl.handle.net/11449/199972 |
Resumo: | Introduction - As maturity plays an important role during controlled atmosphere (CA) storage, the objective of this study was to evaluate if mangoes sorted based on visual characteristics behave differently during CA storage than mangoes sorted based on 150 g kg-1 dry matter (DM) content using near-infrared (Vis-NIR) spectrometer. Materials and methods - 'Palmer' mangoes were harvested and DM predicted by partial least squares regression (PLSR). Fruit quality was evaluated at harvest, after 30 d of CA storage, and after 30 d plus 4 d at ambient conditions. Results and discussion - PLSR model developed with fruit from 2015/2016 and 2016/2017 seasons was able to predict DM content from mangoes produced in a different region (Petrolina, PE), but with high root mean square error of prediction (RMSEP = 20.2 g kg-1) and low R2P (0.19). Therefore, Vis-NIR spectra from mangoes produced in Petrolina, PE were incorporated into the data set and a new model was developed (RMSEv = 13.8 g kg-1, and R2V = 0.63). With the new PLSR model it was possible to sort mangoes produced in Petrolina, PE with 150 g kg-1 DM. Quality differences were not observed between fruit sorted based on 150 g kg-1 DM and based on visual appearance. However, the mangoes sorted based on 150 g kg-1 DM presented lower standard deviation, indicating a more homogeneous fruit batch. Conclusion - The use of portable Vis-NIR spectrometer allows a more uniform sorting of mangoes, which can be used to improve the quality of mangoes that reach the consumer. |
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Repositório Institucional da UNESP |
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Postharvest behavior of mangoes nondestructively sorted based on dry matter content during and after storage under controlled atmosphereChemometricsDry matterMangifera indica L. 'Palmer'Partial least squares regressionPortable visible-near infrared (Vis-NIR) spectrometerIntroduction - As maturity plays an important role during controlled atmosphere (CA) storage, the objective of this study was to evaluate if mangoes sorted based on visual characteristics behave differently during CA storage than mangoes sorted based on 150 g kg-1 dry matter (DM) content using near-infrared (Vis-NIR) spectrometer. Materials and methods - 'Palmer' mangoes were harvested and DM predicted by partial least squares regression (PLSR). Fruit quality was evaluated at harvest, after 30 d of CA storage, and after 30 d plus 4 d at ambient conditions. Results and discussion - PLSR model developed with fruit from 2015/2016 and 2016/2017 seasons was able to predict DM content from mangoes produced in a different region (Petrolina, PE), but with high root mean square error of prediction (RMSEP = 20.2 g kg-1) and low R2P (0.19). Therefore, Vis-NIR spectra from mangoes produced in Petrolina, PE were incorporated into the data set and a new model was developed (RMSEv = 13.8 g kg-1, and R2V = 0.63). With the new PLSR model it was possible to sort mangoes produced in Petrolina, PE with 150 g kg-1 DM. Quality differences were not observed between fruit sorted based on 150 g kg-1 DM and based on visual appearance. However, the mangoes sorted based on 150 g kg-1 DM presented lower standard deviation, indicating a more homogeneous fruit batch. Conclusion - The use of portable Vis-NIR spectrometer allows a more uniform sorting of mangoes, which can be used to improve the quality of mangoes that reach the consumer.Universidade Estadual Paulista Faculdade de Ciências Agrárias e Veterinárias Campus de Jaboticabal, Via de Acesso Prof. Paulo Donato Castellane s/nUniversidade Estadual Paulista (UNESP) Faculdade de Ciências Farmacêuticas (FCFAR) Departamento de Alimentos e Nutrição Campus de Araraquara, Rodovia Araraquara-Jaú, km 1 - CP 502Universidade Federal de Goiás Escola de Agronomia Setor de Horticultura Campus Samambaia, Rodovia Goiânia Nova Veneza km 0Empresa Brasileira de Pesquisa Agropecuária, Semiárido Rodovia BR-428 Km 152 s/nUniversidade de Lisboa Instituto Superior de AgronomiaUniversidade Estadual Paulista Faculdade de Ciências Agrárias e Veterinárias Campus de Jaboticabal, Via de Acesso Prof. Paulo Donato Castellane s/nUniversidade Estadual Paulista (UNESP) Faculdade de Ciências Farmacêuticas (FCFAR) Departamento de Alimentos e Nutrição Campus de Araraquara, Rodovia Araraquara-Jaú, km 1 - CP 502Universidade Estadual Paulista (Unesp)Universidade Federal de Goiás (UFG)Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA)Instituto Superior de AgronomiaDos Santos Neto, J. P. [UNESP]De Carvalho, L. C. [UNESP]Leite, G. W.P. [UNESP]Cunha Júnior, L. C.Gratão, P. L. [UNESP]De Freitas, S. T.Almeida, D. P.F.De Almeida Teixeira, G. H. [UNESP]2020-12-12T01:54:11Z2020-12-12T01:54:11Z2019-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article294-302http://dx.doi.org/10.17660/th2019/74.6.4Fruits, v. 74, n. 6, p. 294-302, 2019.1625-967X0248-1294http://hdl.handle.net/11449/19997210.17660/th2019/74.6.42-s2.0-85078167639Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengFruitsinfo:eu-repo/semantics/openAccess2021-10-23T10:18:20Zoai:repositorio.unesp.br:11449/199972Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T10:18:20Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Postharvest behavior of mangoes nondestructively sorted based on dry matter content during and after storage under controlled atmosphere |
title |
Postharvest behavior of mangoes nondestructively sorted based on dry matter content during and after storage under controlled atmosphere |
spellingShingle |
Postharvest behavior of mangoes nondestructively sorted based on dry matter content during and after storage under controlled atmosphere Dos Santos Neto, J. P. [UNESP] Chemometrics Dry matter Mangifera indica L. 'Palmer' Partial least squares regression Portable visible-near infrared (Vis-NIR) spectrometer |
title_short |
Postharvest behavior of mangoes nondestructively sorted based on dry matter content during and after storage under controlled atmosphere |
title_full |
Postharvest behavior of mangoes nondestructively sorted based on dry matter content during and after storage under controlled atmosphere |
title_fullStr |
Postharvest behavior of mangoes nondestructively sorted based on dry matter content during and after storage under controlled atmosphere |
title_full_unstemmed |
Postharvest behavior of mangoes nondestructively sorted based on dry matter content during and after storage under controlled atmosphere |
title_sort |
Postharvest behavior of mangoes nondestructively sorted based on dry matter content during and after storage under controlled atmosphere |
author |
Dos Santos Neto, J. P. [UNESP] |
author_facet |
Dos Santos Neto, J. P. [UNESP] De Carvalho, L. C. [UNESP] Leite, G. W.P. [UNESP] Cunha Júnior, L. C. Gratão, P. L. [UNESP] De Freitas, S. T. Almeida, D. P.F. De Almeida Teixeira, G. H. [UNESP] |
author_role |
author |
author2 |
De Carvalho, L. C. [UNESP] Leite, G. W.P. [UNESP] Cunha Júnior, L. C. Gratão, P. L. [UNESP] De Freitas, S. T. Almeida, D. P.F. De Almeida Teixeira, G. H. [UNESP] |
author2_role |
author author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Universidade Federal de Goiás (UFG) Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA) Instituto Superior de Agronomia |
dc.contributor.author.fl_str_mv |
Dos Santos Neto, J. P. [UNESP] De Carvalho, L. C. [UNESP] Leite, G. W.P. [UNESP] Cunha Júnior, L. C. Gratão, P. L. [UNESP] De Freitas, S. T. Almeida, D. P.F. De Almeida Teixeira, G. H. [UNESP] |
dc.subject.por.fl_str_mv |
Chemometrics Dry matter Mangifera indica L. 'Palmer' Partial least squares regression Portable visible-near infrared (Vis-NIR) spectrometer |
topic |
Chemometrics Dry matter Mangifera indica L. 'Palmer' Partial least squares regression Portable visible-near infrared (Vis-NIR) spectrometer |
description |
Introduction - As maturity plays an important role during controlled atmosphere (CA) storage, the objective of this study was to evaluate if mangoes sorted based on visual characteristics behave differently during CA storage than mangoes sorted based on 150 g kg-1 dry matter (DM) content using near-infrared (Vis-NIR) spectrometer. Materials and methods - 'Palmer' mangoes were harvested and DM predicted by partial least squares regression (PLSR). Fruit quality was evaluated at harvest, after 30 d of CA storage, and after 30 d plus 4 d at ambient conditions. Results and discussion - PLSR model developed with fruit from 2015/2016 and 2016/2017 seasons was able to predict DM content from mangoes produced in a different region (Petrolina, PE), but with high root mean square error of prediction (RMSEP = 20.2 g kg-1) and low R2P (0.19). Therefore, Vis-NIR spectra from mangoes produced in Petrolina, PE were incorporated into the data set and a new model was developed (RMSEv = 13.8 g kg-1, and R2V = 0.63). With the new PLSR model it was possible to sort mangoes produced in Petrolina, PE with 150 g kg-1 DM. Quality differences were not observed between fruit sorted based on 150 g kg-1 DM and based on visual appearance. However, the mangoes sorted based on 150 g kg-1 DM presented lower standard deviation, indicating a more homogeneous fruit batch. Conclusion - The use of portable Vis-NIR spectrometer allows a more uniform sorting of mangoes, which can be used to improve the quality of mangoes that reach the consumer. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-01-01 2020-12-12T01:54:11Z 2020-12-12T01:54:11Z |
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/10.17660/th2019/74.6.4 Fruits, v. 74, n. 6, p. 294-302, 2019. 1625-967X 0248-1294 http://hdl.handle.net/11449/199972 10.17660/th2019/74.6.4 2-s2.0-85078167639 |
url |
http://dx.doi.org/10.17660/th2019/74.6.4 http://hdl.handle.net/11449/199972 |
identifier_str_mv |
Fruits, v. 74, n. 6, p. 294-302, 2019. 1625-967X 0248-1294 10.17660/th2019/74.6.4 2-s2.0-85078167639 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Fruits |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
294-302 |
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_ |
1799965296673423360 |