Determination of quality attributes and ripening stage using vis-nir spectroscopy in intact seriguela and umbu fruits
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Engenharia na Agricultura |
Texto Completo: | https://periodicos.ufv.br/reveng/article/view/12929 |
Resumo: | To produce seriguela and umbu on a large scale, it is important to detect the ripening stages and quality attributes of the fruits, to define the ideal harvest point. Thus, this study aimed to determine, in a non-destructive way, the quality attributes and ripening stages of intact seriguela and umbu fruits using Vis-NIR spectroscopy. A total of 150 seriguela fruits and 150 umbu fruits were used, at different ripening stages, and subjected to spectral analysis and reference laboratory testing to determine total soluble solids (TSS) and firmness. Spectral data were subjected to different pre-processing techniques. Regression and classification models were created through the statistical learning and machine learning methods. The models with the best performance for TSS were RF (R2P = 0.94) and PLSR (R2P = 0.68), and for firmness were PLSR (R2P = 0.92) and RF (R2P = 0.58), for seriguela and umbu, respectively. The model with the best performance in the classification was LDA, with a precision greater than 95% to discriminate the ripening stages of both fruits. Therefore, the Vis-NIR spectroscopy is a potential tool to determine the quality attributes and ripening stages, in a non-destructive way, of intact seriguela and umbu fruits. |
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Engenharia na Agricultura |
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Determination of quality attributes and ripening stage using vis-nir spectroscopy in intact seriguela and umbu fruitsDetermination of quality attributes and ripening stage using vis-nir spectroscopy in intact seriguela and umbu fruitsSpondias purpurea LSpondias tuberosaTotal soluble solidsFirmnessNon-destructive methodsTo produce seriguela and umbu on a large scale, it is important to detect the ripening stages and quality attributes of the fruits, to define the ideal harvest point. Thus, this study aimed to determine, in a non-destructive way, the quality attributes and ripening stages of intact seriguela and umbu fruits using Vis-NIR spectroscopy. A total of 150 seriguela fruits and 150 umbu fruits were used, at different ripening stages, and subjected to spectral analysis and reference laboratory testing to determine total soluble solids (TSS) and firmness. Spectral data were subjected to different pre-processing techniques. Regression and classification models were created through the statistical learning and machine learning methods. The models with the best performance for TSS were RF (R2P = 0.94) and PLSR (R2P = 0.68), and for firmness were PLSR (R2P = 0.92) and RF (R2P = 0.58), for seriguela and umbu, respectively. The model with the best performance in the classification was LDA, with a precision greater than 95% to discriminate the ripening stages of both fruits. Therefore, the Vis-NIR spectroscopy is a potential tool to determine the quality attributes and ripening stages, in a non-destructive way, of intact seriguela and umbu fruits.To produce seriguela and umbu on a large scale, it is important to detect the ripening stages and quality attributes of the fruits, to define the ideal harvest point. Thus, this study aimed to determine, in a non-destructive way, the quality attributes and ripening stages of intact seriguela and umbu fruits using Vis-NIR spectroscopy. A total of 150 seriguela fruits and 150 umbu fruits were used, at different ripening stages, and subjected to spectral analysis and reference laboratory testing to determine total soluble solids (TSS) and firmness. Spectral data were subjected to different pre-processing techniques. Regression and classification models were created through the statistical learning and machine learning methods. The models with the best performance for TSS were RF (R2P = 0.94) and PLSR (R2P = 0.68), and for firmness were PLSR (R2P = 0.92) and RF (R2P = 0.58), for seriguela and umbu, respectively. The model with the best performance in the classification was LDA, with a precision greater than 95% to discriminate the ripening stages of both fruits. Therefore, the Vis-NIR spectroscopy is a potential tool to determine the quality attributes and ripening stages, in a non-destructive way, of intact seriguela and umbu fruits.Universidade Federal de Viçosa - UFV2022-05-26info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://periodicos.ufv.br/reveng/article/view/1292910.13083/reveng.v30i1.12929Engineering in Agriculture; Vol. 30 No. Contínua (2022); 127-141Revista Engenharia na Agricultura - REVENG; v. 30 n. Contínua (2022); 127-1412175-68131414-3984reponame:Engenharia na Agriculturainstname:Universidade Federal de Viçosa (UFV)instacron:UFVenghttps://periodicos.ufv.br/reveng/article/view/12929/7328Copyright (c) 2022 Revista Engenharia na Agricultura - REVENGhttps://creativecommons.org/licenses/by-nc/4.0info:eu-repo/semantics/openAccessde Araujo Souza , PatríciaFerreira , Iara Jeanice SouzaCosta, Daniel dos Santos2023-01-23T14:06:10Zoai:ojs.periodicos.ufv.br:article/12929Revistahttps://periodicos.ufv.br/revengPUBhttps://periodicos.ufv.br/reveng/oairevistaengenharianagricultura@gmail.com||andrerosa@ufv.br||tramitacao.reveng@gmail.com|| reveng@ufv.br2175-68131414-3984opendoar:2023-01-23T14:06:10Engenharia na Agricultura - Universidade Federal de Viçosa (UFV)false |
dc.title.none.fl_str_mv |
Determination of quality attributes and ripening stage using vis-nir spectroscopy in intact seriguela and umbu fruits Determination of quality attributes and ripening stage using vis-nir spectroscopy in intact seriguela and umbu fruits |
title |
Determination of quality attributes and ripening stage using vis-nir spectroscopy in intact seriguela and umbu fruits |
spellingShingle |
Determination of quality attributes and ripening stage using vis-nir spectroscopy in intact seriguela and umbu fruits de Araujo Souza , Patrícia Spondias purpurea L Spondias tuberosa Total soluble solids Firmness Non-destructive methods |
title_short |
Determination of quality attributes and ripening stage using vis-nir spectroscopy in intact seriguela and umbu fruits |
title_full |
Determination of quality attributes and ripening stage using vis-nir spectroscopy in intact seriguela and umbu fruits |
title_fullStr |
Determination of quality attributes and ripening stage using vis-nir spectroscopy in intact seriguela and umbu fruits |
title_full_unstemmed |
Determination of quality attributes and ripening stage using vis-nir spectroscopy in intact seriguela and umbu fruits |
title_sort |
Determination of quality attributes and ripening stage using vis-nir spectroscopy in intact seriguela and umbu fruits |
author |
de Araujo Souza , Patrícia |
author_facet |
de Araujo Souza , Patrícia Ferreira , Iara Jeanice Souza Costa, Daniel dos Santos |
author_role |
author |
author2 |
Ferreira , Iara Jeanice Souza Costa, Daniel dos Santos |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
de Araujo Souza , Patrícia Ferreira , Iara Jeanice Souza Costa, Daniel dos Santos |
dc.subject.por.fl_str_mv |
Spondias purpurea L Spondias tuberosa Total soluble solids Firmness Non-destructive methods |
topic |
Spondias purpurea L Spondias tuberosa Total soluble solids Firmness Non-destructive methods |
description |
To produce seriguela and umbu on a large scale, it is important to detect the ripening stages and quality attributes of the fruits, to define the ideal harvest point. Thus, this study aimed to determine, in a non-destructive way, the quality attributes and ripening stages of intact seriguela and umbu fruits using Vis-NIR spectroscopy. A total of 150 seriguela fruits and 150 umbu fruits were used, at different ripening stages, and subjected to spectral analysis and reference laboratory testing to determine total soluble solids (TSS) and firmness. Spectral data were subjected to different pre-processing techniques. Regression and classification models were created through the statistical learning and machine learning methods. The models with the best performance for TSS were RF (R2P = 0.94) and PLSR (R2P = 0.68), and for firmness were PLSR (R2P = 0.92) and RF (R2P = 0.58), for seriguela and umbu, respectively. The model with the best performance in the classification was LDA, with a precision greater than 95% to discriminate the ripening stages of both fruits. Therefore, the Vis-NIR spectroscopy is a potential tool to determine the quality attributes and ripening stages, in a non-destructive way, of intact seriguela and umbu fruits. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-05-26 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://periodicos.ufv.br/reveng/article/view/12929 10.13083/reveng.v30i1.12929 |
url |
https://periodicos.ufv.br/reveng/article/view/12929 |
identifier_str_mv |
10.13083/reveng.v30i1.12929 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://periodicos.ufv.br/reveng/article/view/12929/7328 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2022 Revista Engenharia na Agricultura - REVENG https://creativecommons.org/licenses/by-nc/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2022 Revista Engenharia na Agricultura - REVENG https://creativecommons.org/licenses/by-nc/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal de Viçosa - UFV |
publisher.none.fl_str_mv |
Universidade Federal de Viçosa - UFV |
dc.source.none.fl_str_mv |
Engineering in Agriculture; Vol. 30 No. Contínua (2022); 127-141 Revista Engenharia na Agricultura - REVENG; v. 30 n. Contínua (2022); 127-141 2175-6813 1414-3984 reponame:Engenharia na Agricultura instname:Universidade Federal de Viçosa (UFV) instacron:UFV |
instname_str |
Universidade Federal de Viçosa (UFV) |
instacron_str |
UFV |
institution |
UFV |
reponame_str |
Engenharia na Agricultura |
collection |
Engenharia na Agricultura |
repository.name.fl_str_mv |
Engenharia na Agricultura - Universidade Federal de Viçosa (UFV) |
repository.mail.fl_str_mv |
revistaengenharianagricultura@gmail.com||andrerosa@ufv.br||tramitacao.reveng@gmail.com|| reveng@ufv.br |
_version_ |
1800211147323867136 |