Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality
Autor(a) principal: | |
---|---|
Data de Publicação: | 2021 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1038/s41598-021-97223-5 http://hdl.handle.net/11449/222381 |
Resumo: | In the agricultural industry, advances in optical imaging technologies based on rapid and non-destructive approaches have contributed to increase food production for the growing population. The present study employed autofluorescence-spectral imaging and machine learning algorithms to develop distinct models for classification of soybean seeds differing in physiological quality after artificial aging. Autofluorescence signals from the 365/400 nm excitation-emission combination (that exhibited a perfect correlation with the total phenols in the embryo) were efficiently able to segregate treatments. Furthermore, it was also possible to demonstrate a strong correlation between autofluorescence-spectral data and several quality indicators, such as early germination and seed tolerance to stressful conditions. The machine learning models developed based on artificial neural network, support vector machine or linear discriminant analysis showed high performance (0.99 accuracy) for classifying seeds with different quality levels. Taken together, our study shows that the physiological potential of soybean seeds is reduced accompanied by changes in the concentration and, probably in the structure of autofluorescent compounds. In addition, altering the autofluorescent properties in seeds impact the photosynthesis apparatus in seedlings. From the practical point of view, autofluorescence-based imaging can be used to check modifications in the optical properties of soybean seed tissues and to consistently discriminate high-and low-vigor seeds. |
id |
UNSP_09e0cf006fd139306a334944e3d3ee11 |
---|---|
oai_identifier_str |
oai:repositorio.unesp.br:11449/222381 |
network_acronym_str |
UNSP |
network_name_str |
Repositório Institucional da UNESP |
repository_id_str |
2946 |
spelling |
Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed qualityIn the agricultural industry, advances in optical imaging technologies based on rapid and non-destructive approaches have contributed to increase food production for the growing population. The present study employed autofluorescence-spectral imaging and machine learning algorithms to develop distinct models for classification of soybean seeds differing in physiological quality after artificial aging. Autofluorescence signals from the 365/400 nm excitation-emission combination (that exhibited a perfect correlation with the total phenols in the embryo) were efficiently able to segregate treatments. Furthermore, it was also possible to demonstrate a strong correlation between autofluorescence-spectral data and several quality indicators, such as early germination and seed tolerance to stressful conditions. The machine learning models developed based on artificial neural network, support vector machine or linear discriminant analysis showed high performance (0.99 accuracy) for classifying seeds with different quality levels. Taken together, our study shows that the physiological potential of soybean seeds is reduced accompanied by changes in the concentration and, probably in the structure of autofluorescent compounds. In addition, altering the autofluorescent properties in seeds impact the photosynthesis apparatus in seedlings. From the practical point of view, autofluorescence-based imaging can be used to check modifications in the optical properties of soybean seed tissues and to consistently discriminate high-and low-vigor seeds.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Center for Nuclear Energy in Agriculture (CENA) University of São Paulo (USP)Department of Crop Science College of Agriculture Luiz de Queiroz (ESALQ) University of São Paulo (USP)Department of Genetics College of Agriculture Luiz de Queiroz (ESALQ) University of São Paulo (USP)Department of Agronomy Federal University of Viçosa (UFV)Department of Biosystems Engineering School of Sciences and Engineering São Paulo State University (UNESP)Department of Biosystems Engineering School of Sciences and Engineering São Paulo State University (UNESP)FAPESP: 2017/15220-7Universidade de São Paulo (USP)Universidade Federal de Viçosa (UFV)Universidade Estadual Paulista (UNESP)Barboza da Silva, ClíssiaOliveira, Nielsen Moreirade Carvalho, Marcia Eugenia Amaralde Medeiros, André Dantasde Lima Nogueira, Marinados Reis, André Rodrigues [UNESP]2022-04-28T19:44:19Z2022-04-28T19:44:19Z2021-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1038/s41598-021-97223-5Scientific Reports, v. 11, n. 1, 2021.2045-2322http://hdl.handle.net/11449/22238110.1038/s41598-021-97223-52-s2.0-85114638542Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengScientific Reportsinfo:eu-repo/semantics/openAccess2022-04-28T19:44:19Zoai:repositorio.unesp.br:11449/222381Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T15:16:34.276729Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality |
title |
Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality |
spellingShingle |
Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality Barboza da Silva, Clíssia |
title_short |
Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality |
title_full |
Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality |
title_fullStr |
Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality |
title_full_unstemmed |
Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality |
title_sort |
Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality |
author |
Barboza da Silva, Clíssia |
author_facet |
Barboza da Silva, Clíssia Oliveira, Nielsen Moreira de Carvalho, Marcia Eugenia Amaral de Medeiros, André Dantas de Lima Nogueira, Marina dos Reis, André Rodrigues [UNESP] |
author_role |
author |
author2 |
Oliveira, Nielsen Moreira de Carvalho, Marcia Eugenia Amaral de Medeiros, André Dantas de Lima Nogueira, Marina dos Reis, André Rodrigues [UNESP] |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade de São Paulo (USP) Universidade Federal de Viçosa (UFV) Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Barboza da Silva, Clíssia Oliveira, Nielsen Moreira de Carvalho, Marcia Eugenia Amaral de Medeiros, André Dantas de Lima Nogueira, Marina dos Reis, André Rodrigues [UNESP] |
description |
In the agricultural industry, advances in optical imaging technologies based on rapid and non-destructive approaches have contributed to increase food production for the growing population. The present study employed autofluorescence-spectral imaging and machine learning algorithms to develop distinct models for classification of soybean seeds differing in physiological quality after artificial aging. Autofluorescence signals from the 365/400 nm excitation-emission combination (that exhibited a perfect correlation with the total phenols in the embryo) were efficiently able to segregate treatments. Furthermore, it was also possible to demonstrate a strong correlation between autofluorescence-spectral data and several quality indicators, such as early germination and seed tolerance to stressful conditions. The machine learning models developed based on artificial neural network, support vector machine or linear discriminant analysis showed high performance (0.99 accuracy) for classifying seeds with different quality levels. Taken together, our study shows that the physiological potential of soybean seeds is reduced accompanied by changes in the concentration and, probably in the structure of autofluorescent compounds. In addition, altering the autofluorescent properties in seeds impact the photosynthesis apparatus in seedlings. From the practical point of view, autofluorescence-based imaging can be used to check modifications in the optical properties of soybean seed tissues and to consistently discriminate high-and low-vigor seeds. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-12-01 2022-04-28T19:44:19Z 2022-04-28T19:44:19Z |
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/10.1038/s41598-021-97223-5 Scientific Reports, v. 11, n. 1, 2021. 2045-2322 http://hdl.handle.net/11449/222381 10.1038/s41598-021-97223-5 2-s2.0-85114638542 |
url |
http://dx.doi.org/10.1038/s41598-021-97223-5 http://hdl.handle.net/11449/222381 |
identifier_str_mv |
Scientific Reports, v. 11, n. 1, 2021. 2045-2322 10.1038/s41598-021-97223-5 2-s2.0-85114638542 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Scientific Reports |
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_ |
1808128493600374784 |