Identification of peanut storage period based on hyperspectral imaging technology
Autor(a) principal: | |
---|---|
Data de Publicação: | 2022 |
Outros Autores: | , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Food Science and Technology (Campinas) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612022000101299 |
Resumo: | Abstract Peanut storage time affected the quality of peanut seed sowing and germination and also affected the taste of edible peanuts. With the increase of peanut storage time, the total amount of water and amino acids decreased, and peanuts appeared moldy. The artificial judgment of peanut storage time mostly relied on visual classification to evaluate the color, which leads to large differences in color classifications between observers. This research was conducted to determine the fresh state of peanuts during storage based on the hyperspectral imaging (HSI) technology, and to identify the storage time of peanuts through hyperspectral images (387~1035 nm). Three models, two preprocessing methods, and two feature band extraction methods were combined. The experimental results shows that the DT-MF-Catboost model was the best method to detect the storage time of peanuts, and its accuracy of identifying the storage time of peanuts was 97.53%. Studies have shown that HSI has great potential in classifying the freshness and identification of peanuts, and provides a basis for non-destructive testing classification as well as grading of peanuts during storage. |
id |
SBCTA-1_d67a445172595b352ea1f45dfd51a4c3 |
---|---|
oai_identifier_str |
oai:scielo:S0101-20612022000101299 |
network_acronym_str |
SBCTA-1 |
network_name_str |
Food Science and Technology (Campinas) |
repository_id_str |
|
spelling |
Identification of peanut storage period based on hyperspectral imaging technologyhyperspectralfreshnessnon-destructive testing techniquesfeature selectionregression modelAbstract Peanut storage time affected the quality of peanut seed sowing and germination and also affected the taste of edible peanuts. With the increase of peanut storage time, the total amount of water and amino acids decreased, and peanuts appeared moldy. The artificial judgment of peanut storage time mostly relied on visual classification to evaluate the color, which leads to large differences in color classifications between observers. This research was conducted to determine the fresh state of peanuts during storage based on the hyperspectral imaging (HSI) technology, and to identify the storage time of peanuts through hyperspectral images (387~1035 nm). Three models, two preprocessing methods, and two feature band extraction methods were combined. The experimental results shows that the DT-MF-Catboost model was the best method to detect the storage time of peanuts, and its accuracy of identifying the storage time of peanuts was 97.53%. Studies have shown that HSI has great potential in classifying the freshness and identification of peanuts, and provides a basis for non-destructive testing classification as well as grading of peanuts during storage.Sociedade Brasileira de Ciência e Tecnologia de Alimentos2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612022000101299Food Science and Technology v.42 2022reponame:Food Science and Technology (Campinas)instname:Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA)instacron:SBCTA10.1590/fst.65822info:eu-repo/semantics/openAccessZOU,ZhiyongCHEN,JieZHOU,ManWANG,ZhitangLIU,KeZHAO,YongpengWANG,YuchaoWU,WeijiaXU,Lijiaeng2022-08-30T00:00:00Zoai:scielo:S0101-20612022000101299Revistahttp://www.scielo.br/ctaONGhttps://old.scielo.br/oai/scielo-oai.php||revista@sbcta.org.br1678-457X0101-2061opendoar:2022-08-30T00:00Food Science and Technology (Campinas) - Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA)false |
dc.title.none.fl_str_mv |
Identification of peanut storage period based on hyperspectral imaging technology |
title |
Identification of peanut storage period based on hyperspectral imaging technology |
spellingShingle |
Identification of peanut storage period based on hyperspectral imaging technology ZOU,Zhiyong hyperspectral freshness non-destructive testing techniques feature selection regression model |
title_short |
Identification of peanut storage period based on hyperspectral imaging technology |
title_full |
Identification of peanut storage period based on hyperspectral imaging technology |
title_fullStr |
Identification of peanut storage period based on hyperspectral imaging technology |
title_full_unstemmed |
Identification of peanut storage period based on hyperspectral imaging technology |
title_sort |
Identification of peanut storage period based on hyperspectral imaging technology |
author |
ZOU,Zhiyong |
author_facet |
ZOU,Zhiyong CHEN,Jie ZHOU,Man WANG,Zhitang LIU,Ke ZHAO,Yongpeng WANG,Yuchao WU,Weijia XU,Lijia |
author_role |
author |
author2 |
CHEN,Jie ZHOU,Man WANG,Zhitang LIU,Ke ZHAO,Yongpeng WANG,Yuchao WU,Weijia XU,Lijia |
author2_role |
author author author author author author author author |
dc.contributor.author.fl_str_mv |
ZOU,Zhiyong CHEN,Jie ZHOU,Man WANG,Zhitang LIU,Ke ZHAO,Yongpeng WANG,Yuchao WU,Weijia XU,Lijia |
dc.subject.por.fl_str_mv |
hyperspectral freshness non-destructive testing techniques feature selection regression model |
topic |
hyperspectral freshness non-destructive testing techniques feature selection regression model |
description |
Abstract Peanut storage time affected the quality of peanut seed sowing and germination and also affected the taste of edible peanuts. With the increase of peanut storage time, the total amount of water and amino acids decreased, and peanuts appeared moldy. The artificial judgment of peanut storage time mostly relied on visual classification to evaluate the color, which leads to large differences in color classifications between observers. This research was conducted to determine the fresh state of peanuts during storage based on the hyperspectral imaging (HSI) technology, and to identify the storage time of peanuts through hyperspectral images (387~1035 nm). Three models, two preprocessing methods, and two feature band extraction methods were combined. The experimental results shows that the DT-MF-Catboost model was the best method to detect the storage time of peanuts, and its accuracy of identifying the storage time of peanuts was 97.53%. Studies have shown that HSI has great potential in classifying the freshness and identification of peanuts, and provides a basis for non-destructive testing classification as well as grading of peanuts during storage. |
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=S0101-20612022000101299 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612022000101299 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/fst.65822 |
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 |
Sociedade Brasileira de Ciência e Tecnologia de Alimentos |
publisher.none.fl_str_mv |
Sociedade Brasileira de Ciência e Tecnologia de Alimentos |
dc.source.none.fl_str_mv |
Food Science and Technology v.42 2022 reponame:Food Science and Technology (Campinas) instname:Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA) instacron:SBCTA |
instname_str |
Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA) |
instacron_str |
SBCTA |
institution |
SBCTA |
reponame_str |
Food Science and Technology (Campinas) |
collection |
Food Science and Technology (Campinas) |
repository.name.fl_str_mv |
Food Science and Technology (Campinas) - Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA) |
repository.mail.fl_str_mv |
||revista@sbcta.org.br |
_version_ |
1752126335105368064 |