DETERMINATION OF QUALITY AND RIPENING STAGES OF ‘PACOVAN’ BANANAS USING VIS-NIR SPECTROSCOPY AND MACHINE LEARNING
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Engenharia Agrícola |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162022000800103 |
Resumo: | ABSTRACT This paper aimed to develop predictive models to determine total soluble solids, firmness, and ripening stages of ‘Pacovan’ bananas, using Vis-NIR spectroscopy and machine learning algorithms. A total of 384 bananas were divided into different days of storage (0, 3, 6, 9, 12, 15, 18, and 21 days) at two temperatures (25°C and 20°C). Bananas were subjected to spectral analysis using a spectrometer operating in spectral range of 350 – 2500 nm. Physicochemical parameters of quality, total soluble solids, and firmness were determined by reference analyses. Different machine learning algorithms were used to develop regression models and supervised classification. The best model for total soluble solids was the Random Forest with variable selection, showing an R2cv of 0.90 and RMSECV of 2.31. The best model for firmness was the Support Vector Machine with variable selection, showing an R2cv of 0.84 and RMSECV of 7.98. The best classification model for different ripening stages was the Multilayer Perceptron with variable selection, which achieved the precision of 74.22%. Therefore, Vis-NIR spectroscopy associated with machine learning algorithms is a promising tool for monitoring the quality and ripening stages of ‘Pacovan’ bananas. |
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DETERMINATION OF QUALITY AND RIPENING STAGES OF ‘PACOVAN’ BANANAS USING VIS-NIR SPECTROSCOPY AND MACHINE LEARNINGquality attributesnon-destructive methodMusa spp.ABSTRACT This paper aimed to develop predictive models to determine total soluble solids, firmness, and ripening stages of ‘Pacovan’ bananas, using Vis-NIR spectroscopy and machine learning algorithms. A total of 384 bananas were divided into different days of storage (0, 3, 6, 9, 12, 15, 18, and 21 days) at two temperatures (25°C and 20°C). Bananas were subjected to spectral analysis using a spectrometer operating in spectral range of 350 – 2500 nm. Physicochemical parameters of quality, total soluble solids, and firmness were determined by reference analyses. Different machine learning algorithms were used to develop regression models and supervised classification. The best model for total soluble solids was the Random Forest with variable selection, showing an R2cv of 0.90 and RMSECV of 2.31. The best model for firmness was the Support Vector Machine with variable selection, showing an R2cv of 0.84 and RMSECV of 7.98. The best classification model for different ripening stages was the Multilayer Perceptron with variable selection, which achieved the precision of 74.22%. Therefore, Vis-NIR spectroscopy associated with machine learning algorithms is a promising tool for monitoring the quality and ripening stages of ‘Pacovan’ bananas.Associação Brasileira de Engenharia Agrícola2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162022000800103Engenharia Agrícola v.42 n.spe 2022reponame:Engenharia Agrícolainstname:Associação Brasileira de Engenharia Agrícola (SBEA)instacron:SBEA10.1590/1809-4430-eng.agric.v42nepe20210160/2022info:eu-repo/semantics/openAccessFerreira,Iara J. S.Almeida,Sarah L. F. de O.Figueiredo Neto,AcácioCosta,Daniel dos Santoseng2022-03-24T00:00:00Zoai:scielo:S0100-69162022000800103Revistahttp://www.engenhariaagricola.org.br/ORGhttps://old.scielo.br/oai/scielo-oai.phprevistasbea@sbea.org.br||sbea@sbea.org.br1809-44300100-6916opendoar:2022-03-24T00:00Engenharia Agrícola - Associação Brasileira de Engenharia Agrícola (SBEA)false |
dc.title.none.fl_str_mv |
DETERMINATION OF QUALITY AND RIPENING STAGES OF ‘PACOVAN’ BANANAS USING VIS-NIR SPECTROSCOPY AND MACHINE LEARNING |
title |
DETERMINATION OF QUALITY AND RIPENING STAGES OF ‘PACOVAN’ BANANAS USING VIS-NIR SPECTROSCOPY AND MACHINE LEARNING |
spellingShingle |
DETERMINATION OF QUALITY AND RIPENING STAGES OF ‘PACOVAN’ BANANAS USING VIS-NIR SPECTROSCOPY AND MACHINE LEARNING Ferreira,Iara J. S. quality attributes non-destructive method Musa spp. |
title_short |
DETERMINATION OF QUALITY AND RIPENING STAGES OF ‘PACOVAN’ BANANAS USING VIS-NIR SPECTROSCOPY AND MACHINE LEARNING |
title_full |
DETERMINATION OF QUALITY AND RIPENING STAGES OF ‘PACOVAN’ BANANAS USING VIS-NIR SPECTROSCOPY AND MACHINE LEARNING |
title_fullStr |
DETERMINATION OF QUALITY AND RIPENING STAGES OF ‘PACOVAN’ BANANAS USING VIS-NIR SPECTROSCOPY AND MACHINE LEARNING |
title_full_unstemmed |
DETERMINATION OF QUALITY AND RIPENING STAGES OF ‘PACOVAN’ BANANAS USING VIS-NIR SPECTROSCOPY AND MACHINE LEARNING |
title_sort |
DETERMINATION OF QUALITY AND RIPENING STAGES OF ‘PACOVAN’ BANANAS USING VIS-NIR SPECTROSCOPY AND MACHINE LEARNING |
author |
Ferreira,Iara J. S. |
author_facet |
Ferreira,Iara J. S. Almeida,Sarah L. F. de O. Figueiredo Neto,Acácio Costa,Daniel dos Santos |
author_role |
author |
author2 |
Almeida,Sarah L. F. de O. Figueiredo Neto,Acácio Costa,Daniel dos Santos |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Ferreira,Iara J. S. Almeida,Sarah L. F. de O. Figueiredo Neto,Acácio Costa,Daniel dos Santos |
dc.subject.por.fl_str_mv |
quality attributes non-destructive method Musa spp. |
topic |
quality attributes non-destructive method Musa spp. |
description |
ABSTRACT This paper aimed to develop predictive models to determine total soluble solids, firmness, and ripening stages of ‘Pacovan’ bananas, using Vis-NIR spectroscopy and machine learning algorithms. A total of 384 bananas were divided into different days of storage (0, 3, 6, 9, 12, 15, 18, and 21 days) at two temperatures (25°C and 20°C). Bananas were subjected to spectral analysis using a spectrometer operating in spectral range of 350 – 2500 nm. Physicochemical parameters of quality, total soluble solids, and firmness were determined by reference analyses. Different machine learning algorithms were used to develop regression models and supervised classification. The best model for total soluble solids was the Random Forest with variable selection, showing an R2cv of 0.90 and RMSECV of 2.31. The best model for firmness was the Support Vector Machine with variable selection, showing an R2cv of 0.84 and RMSECV of 7.98. The best classification model for different ripening stages was the Multilayer Perceptron with variable selection, which achieved the precision of 74.22%. Therefore, Vis-NIR spectroscopy associated with machine learning algorithms is a promising tool for monitoring the quality and ripening stages of ‘Pacovan’ bananas. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162022000800103 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162022000800103 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1809-4430-eng.agric.v42nepe20210160/2022 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Associação Brasileira de Engenharia Agrícola |
publisher.none.fl_str_mv |
Associação Brasileira de Engenharia Agrícola |
dc.source.none.fl_str_mv |
Engenharia Agrícola v.42 n.spe 2022 reponame:Engenharia Agrícola instname:Associação Brasileira de Engenharia Agrícola (SBEA) instacron:SBEA |
instname_str |
Associação Brasileira de Engenharia Agrícola (SBEA) |
instacron_str |
SBEA |
institution |
SBEA |
reponame_str |
Engenharia Agrícola |
collection |
Engenharia Agrícola |
repository.name.fl_str_mv |
Engenharia Agrícola - Associação Brasileira de Engenharia Agrícola (SBEA) |
repository.mail.fl_str_mv |
revistasbea@sbea.org.br||sbea@sbea.org.br |
_version_ |
1752126275471802368 |