Prediction of fatty acids in rice storage based on odor characteristics by gas chromatography-ion mobility spectrometry
Autor(a) principal: | |
---|---|
Data de Publicação: | 2023 |
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=S0101-20612023000100432 |
Resumo: | Abstract In order to develop a quick method for predicting fatty acid in rice storage, gas chromatography-ion mobility spectrometry (GC-IMS) was applied to detect and analyze volatile organic compounds (VOCs) at different rice storage stages, and partial least squares regression (PLSR) algorithm was used to establish a linear regression model between fatty acid values and characteristic VOCs. The results showed that rice fatty acid values increased gradually with extension of storage time. Odor components of rice mainly included alcohols and aldehydes. Except for 1-octene-3-alcohol, the content of other VOCs showed an overall downward trend during the storage period. After variable optimization using two different algorithms, the correlation coefficient of the PLSR cross validation model could reach 0.9544, and the corresponding root mean square error was 2.4093. In conclusion, fatty acid values of rice with different storage periods could be accurately predicted by using characteristic VOCs variables and chemometric tools, which would provide a rapid and nondestructive detection method for rice quality during storage based on odor information. |
id |
SBCTA-1_89d3170cc178b811736a0aa82562eb4d |
---|---|
oai_identifier_str |
oai:scielo:S0101-20612023000100432 |
network_acronym_str |
SBCTA-1 |
network_name_str |
Food Science and Technology (Campinas) |
repository_id_str |
|
spelling |
Prediction of fatty acids in rice storage based on odor characteristics by gas chromatography-ion mobility spectrometryriceodor characteristicsfatty acidspartial squares regressiongas chromatography-ion mobility spectrometryAbstract In order to develop a quick method for predicting fatty acid in rice storage, gas chromatography-ion mobility spectrometry (GC-IMS) was applied to detect and analyze volatile organic compounds (VOCs) at different rice storage stages, and partial least squares regression (PLSR) algorithm was used to establish a linear regression model between fatty acid values and characteristic VOCs. The results showed that rice fatty acid values increased gradually with extension of storage time. Odor components of rice mainly included alcohols and aldehydes. Except for 1-octene-3-alcohol, the content of other VOCs showed an overall downward trend during the storage period. After variable optimization using two different algorithms, the correlation coefficient of the PLSR cross validation model could reach 0.9544, and the corresponding root mean square error was 2.4093. In conclusion, fatty acid values of rice with different storage periods could be accurately predicted by using characteristic VOCs variables and chemometric tools, which would provide a rapid and nondestructive detection method for rice quality during storage based on odor information.Sociedade Brasileira de Ciência e Tecnologia de Alimentos2023-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612023000100432Food Science and Technology v.43 2023reponame:Food Science and Technology (Campinas)instname:Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA)instacron:SBCTA10.1590/fst.99822info:eu-repo/semantics/openAccessCHEN,TongZHOU,ChuanyueLI,HaiyuCHEN,BinWANG,YongCHENG,QianweiMENG,Lulieng2022-11-25T00:00:00Zoai:scielo:S0101-20612023000100432Revistahttp://www.scielo.br/ctaONGhttps://old.scielo.br/oai/scielo-oai.php||revista@sbcta.org.br1678-457X0101-2061opendoar:2022-11-25T00:00Food Science and Technology (Campinas) - Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA)false |
dc.title.none.fl_str_mv |
Prediction of fatty acids in rice storage based on odor characteristics by gas chromatography-ion mobility spectrometry |
title |
Prediction of fatty acids in rice storage based on odor characteristics by gas chromatography-ion mobility spectrometry |
spellingShingle |
Prediction of fatty acids in rice storage based on odor characteristics by gas chromatography-ion mobility spectrometry CHEN,Tong rice odor characteristics fatty acids partial squares regression gas chromatography-ion mobility spectrometry |
title_short |
Prediction of fatty acids in rice storage based on odor characteristics by gas chromatography-ion mobility spectrometry |
title_full |
Prediction of fatty acids in rice storage based on odor characteristics by gas chromatography-ion mobility spectrometry |
title_fullStr |
Prediction of fatty acids in rice storage based on odor characteristics by gas chromatography-ion mobility spectrometry |
title_full_unstemmed |
Prediction of fatty acids in rice storage based on odor characteristics by gas chromatography-ion mobility spectrometry |
title_sort |
Prediction of fatty acids in rice storage based on odor characteristics by gas chromatography-ion mobility spectrometry |
author |
CHEN,Tong |
author_facet |
CHEN,Tong ZHOU,Chuanyue LI,Haiyu CHEN,Bin WANG,Yong CHENG,Qianwei MENG,Luli |
author_role |
author |
author2 |
ZHOU,Chuanyue LI,Haiyu CHEN,Bin WANG,Yong CHENG,Qianwei MENG,Luli |
author2_role |
author author author author author author |
dc.contributor.author.fl_str_mv |
CHEN,Tong ZHOU,Chuanyue LI,Haiyu CHEN,Bin WANG,Yong CHENG,Qianwei MENG,Luli |
dc.subject.por.fl_str_mv |
rice odor characteristics fatty acids partial squares regression gas chromatography-ion mobility spectrometry |
topic |
rice odor characteristics fatty acids partial squares regression gas chromatography-ion mobility spectrometry |
description |
Abstract In order to develop a quick method for predicting fatty acid in rice storage, gas chromatography-ion mobility spectrometry (GC-IMS) was applied to detect and analyze volatile organic compounds (VOCs) at different rice storage stages, and partial least squares regression (PLSR) algorithm was used to establish a linear regression model between fatty acid values and characteristic VOCs. The results showed that rice fatty acid values increased gradually with extension of storage time. Odor components of rice mainly included alcohols and aldehydes. Except for 1-octene-3-alcohol, the content of other VOCs showed an overall downward trend during the storage period. After variable optimization using two different algorithms, the correlation coefficient of the PLSR cross validation model could reach 0.9544, and the corresponding root mean square error was 2.4093. In conclusion, fatty acid values of rice with different storage periods could be accurately predicted by using characteristic VOCs variables and chemometric tools, which would provide a rapid and nondestructive detection method for rice quality during storage based on odor information. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-01-01 |
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=S0101-20612023000100432 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612023000100432 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/fst.99822 |
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.43 2023 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_ |
1752126336156041216 |