Determination of quality attributes and ripening stage using vis-nir spectroscopy in intact seriguela and umbu fruits

Detalhes bibliográficos
Autor(a) principal: de Araujo Souza , Patrícia
Data de Publicação: 2022
Outros Autores: Ferreira , Iara Jeanice Souza, Costa, Daniel dos Santos
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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spelling 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
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