Quantitative structure-retention relationships analysis of retention index of essential oils

Detalhes bibliográficos
Autor(a) principal: Noorizadeh,Hadi
Data de Publicação: 2011
Outros Autores: Farmany,Abbas, Noorizadeh,Mehrab
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Química Nova (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-40422011000200014
Resumo: Genetic algorithm and multiple linear regression (GA-MLR), partial least square (GA-PLS), kernel PLS (GA-KPLS) and Levenberg-Marquardt artificial neural network (L-M ANN) techniques were used to investigate the correlation between retention index (RI) and descriptors for 116 diverse compounds in essential oils of six Stachys species. The correlation coefficient LGO-CV (Q²) between experimental and predicted RI for test set by GA-MLR, GA-PLS, GA-KPLS and L-M ANN was 0.886, 0.912, 0.937 and 0.964, respectively. This is the first research on the QSRR of the essential oil compounds against the RI using the GA-KPLS and L-M ANN.
id SBQ-3_28ec7cfee24f86f0b6772a79dc37e5fa
oai_identifier_str oai:scielo:S0100-40422011000200014
network_acronym_str SBQ-3
network_name_str Química Nova (Online)
repository_id_str
spelling Quantitative structure-retention relationships analysis of retention index of essential oilsessential oilsgenetic algorithm-kernel partial least squaresLevenberg-Marquardt artificial neural networkGenetic algorithm and multiple linear regression (GA-MLR), partial least square (GA-PLS), kernel PLS (GA-KPLS) and Levenberg-Marquardt artificial neural network (L-M ANN) techniques were used to investigate the correlation between retention index (RI) and descriptors for 116 diverse compounds in essential oils of six Stachys species. The correlation coefficient LGO-CV (Q²) between experimental and predicted RI for test set by GA-MLR, GA-PLS, GA-KPLS and L-M ANN was 0.886, 0.912, 0.937 and 0.964, respectively. This is the first research on the QSRR of the essential oil compounds against the RI using the GA-KPLS and L-M ANN.Sociedade Brasileira de Química2011-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-40422011000200014Química Nova v.34 n.2 2011reponame:Química Nova (Online)instname:Sociedade Brasileira de Química (SBQ)instacron:SBQ10.1590/S0100-40422011000200014info:eu-repo/semantics/openAccessNoorizadeh,HadiFarmany,AbbasNoorizadeh,Mehrabeng2011-03-14T00:00:00Zoai:scielo:S0100-40422011000200014Revistahttps://www.scielo.br/j/qn/ONGhttps://old.scielo.br/oai/scielo-oai.phpquimicanova@sbq.org.br1678-70640100-4042opendoar:2011-03-14T00:00Química Nova (Online) - Sociedade Brasileira de Química (SBQ)false
dc.title.none.fl_str_mv Quantitative structure-retention relationships analysis of retention index of essential oils
title Quantitative structure-retention relationships analysis of retention index of essential oils
spellingShingle Quantitative structure-retention relationships analysis of retention index of essential oils
Noorizadeh,Hadi
essential oils
genetic algorithm-kernel partial least squares
Levenberg-Marquardt artificial neural network
title_short Quantitative structure-retention relationships analysis of retention index of essential oils
title_full Quantitative structure-retention relationships analysis of retention index of essential oils
title_fullStr Quantitative structure-retention relationships analysis of retention index of essential oils
title_full_unstemmed Quantitative structure-retention relationships analysis of retention index of essential oils
title_sort Quantitative structure-retention relationships analysis of retention index of essential oils
author Noorizadeh,Hadi
author_facet Noorizadeh,Hadi
Farmany,Abbas
Noorizadeh,Mehrab
author_role author
author2 Farmany,Abbas
Noorizadeh,Mehrab
author2_role author
author
dc.contributor.author.fl_str_mv Noorizadeh,Hadi
Farmany,Abbas
Noorizadeh,Mehrab
dc.subject.por.fl_str_mv essential oils
genetic algorithm-kernel partial least squares
Levenberg-Marquardt artificial neural network
topic essential oils
genetic algorithm-kernel partial least squares
Levenberg-Marquardt artificial neural network
description Genetic algorithm and multiple linear regression (GA-MLR), partial least square (GA-PLS), kernel PLS (GA-KPLS) and Levenberg-Marquardt artificial neural network (L-M ANN) techniques were used to investigate the correlation between retention index (RI) and descriptors for 116 diverse compounds in essential oils of six Stachys species. The correlation coefficient LGO-CV (Q²) between experimental and predicted RI for test set by GA-MLR, GA-PLS, GA-KPLS and L-M ANN was 0.886, 0.912, 0.937 and 0.964, respectively. This is the first research on the QSRR of the essential oil compounds against the RI using the GA-KPLS and L-M ANN.
publishDate 2011
dc.date.none.fl_str_mv 2011-01-01
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=S0100-40422011000200014
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-40422011000200014
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/S0100-40422011000200014
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 Química
publisher.none.fl_str_mv Sociedade Brasileira de Química
dc.source.none.fl_str_mv Química Nova v.34 n.2 2011
reponame:Química Nova (Online)
instname:Sociedade Brasileira de Química (SBQ)
instacron:SBQ
instname_str Sociedade Brasileira de Química (SBQ)
instacron_str SBQ
institution SBQ
reponame_str Química Nova (Online)
collection Química Nova (Online)
repository.name.fl_str_mv Química Nova (Online) - Sociedade Brasileira de Química (SBQ)
repository.mail.fl_str_mv quimicanova@sbq.org.br
_version_ 1750318111835291648

Registros relacionados