QSPR analysis for the retention index of flavors and fragrances on a OV-101 column

Autores
Rojas, Cristian; Pis Diez, Reinaldo; Tripaldi, Piercosimo; Duchowicz, Pablo Román
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
A predictive quantitative structure`property relationships (QSPR) is developed for modeling the retention index measured on the OV-101 glass capillary gas chromatography column, in a set of 1208 flavor and fragrance compounds. The 4885 molecular descriptors are calculated using the Dragon software and then are simultaneously analyzed through multivariable linear regression analysis using the replacement method (RM) variable subset selection technique. We proceed in three steps, the first one by considering all descriptor blocks, the second one by excluding conformational descriptors blocks, and the last one by analyzing only 3D-descriptors families. The models are properly validated through an external test set of compounds. Cross-validation methods such as leave-one-out and leave-more-out are applied, togetherwith Y-randomization and applicability domain analysis. The results clearly suggest that 3D-descriptors do not offer relevant information for modeling the retention index, while a topological index such as the solvation connectivity index of first order has a high relevance for this purpose.
Fil: Rojas, Cristian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico la Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina
Fil: Pis Diez, Reinaldo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina
Fil: Tripaldi, Piercosimo. Universidad del Azuay; Ecuador
Fil: Duchowicz, Pablo Román. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico La Plata. Centro de Química Inorgánica; Argentina
Materia
Flavors And Fragrances
Ov-101 Column
Qspr Theory
Dragon Software
Replacement Method
K-Means Cluster Analysis
Total-Order Ranking
Dart
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/7556
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/7556
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling QSPR analysis for the retention index of flavors and fragrances on a OV-101 columnRojas, CristianPis Diez, ReinaldoTripaldi, PiercosimoDuchowicz, Pablo RománFlavors And FragrancesOv-101 ColumnQspr TheoryDragon SoftwareReplacement MethodK-Means Cluster AnalysisTotal-Order RankingDarthttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1A predictive quantitative structure`property relationships (QSPR) is developed for modeling the retention index measured on the OV-101 glass capillary gas chromatography column, in a set of 1208 flavor and fragrance compounds. The 4885 molecular descriptors are calculated using the Dragon software and then are simultaneously analyzed through multivariable linear regression analysis using the replacement method (RM) variable subset selection technique. We proceed in three steps, the first one by considering all descriptor blocks, the second one by excluding conformational descriptors blocks, and the last one by analyzing only 3D-descriptors families. The models are properly validated through an external test set of compounds. Cross-validation methods such as leave-one-out and leave-more-out are applied, togetherwith Y-randomization and applicability domain analysis. The results clearly suggest that 3D-descriptors do not offer relevant information for modeling the retention index, while a topological index such as the solvation connectivity index of first order has a high relevance for this purpose.Fil: Rojas, Cristian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico la Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; ArgentinaFil: Pis Diez, Reinaldo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; ArgentinaFil: Tripaldi, Piercosimo. Universidad del Azuay; EcuadorFil: Duchowicz, Pablo Román. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico La Plata. Centro de Química Inorgánica; ArgentinaElsevier Science2015-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/7556Rojas, Cristian; Pis Diez, Reinaldo; Tripaldi, Piercosimo; Duchowicz, Pablo Román; QSPR analysis for the retention index of flavors and fragrances on a OV-101 column; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 140; 1-2015; 126-1320169-7439enginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0169743914002366info:eu-repo/semantics/altIdentifier/doi/10.1016/j.chemolab.2014.09.020info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:40:47Zoai:ri.conicet.gov.ar:11336/7556instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:40:48.218CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv QSPR analysis for the retention index of flavors and fragrances on a OV-101 column
title QSPR analysis for the retention index of flavors and fragrances on a OV-101 column
spellingShingle QSPR analysis for the retention index of flavors and fragrances on a OV-101 column
Rojas, Cristian
Flavors And Fragrances
Ov-101 Column
Qspr Theory
Dragon Software
Replacement Method
K-Means Cluster Analysis
Total-Order Ranking
Dart
title_short QSPR analysis for the retention index of flavors and fragrances on a OV-101 column
title_full QSPR analysis for the retention index of flavors and fragrances on a OV-101 column
title_fullStr QSPR analysis for the retention index of flavors and fragrances on a OV-101 column
title_full_unstemmed QSPR analysis for the retention index of flavors and fragrances on a OV-101 column
title_sort QSPR analysis for the retention index of flavors and fragrances on a OV-101 column
dc.creator.none.fl_str_mv Rojas, Cristian
Pis Diez, Reinaldo
Tripaldi, Piercosimo
Duchowicz, Pablo Román
author Rojas, Cristian
author_facet Rojas, Cristian
Pis Diez, Reinaldo
Tripaldi, Piercosimo
Duchowicz, Pablo Román
author_role author
author2 Pis Diez, Reinaldo
Tripaldi, Piercosimo
Duchowicz, Pablo Román
author2_role author
author
author
dc.subject.none.fl_str_mv Flavors And Fragrances
Ov-101 Column
Qspr Theory
Dragon Software
Replacement Method
K-Means Cluster Analysis
Total-Order Ranking
Dart
topic Flavors And Fragrances
Ov-101 Column
Qspr Theory
Dragon Software
Replacement Method
K-Means Cluster Analysis
Total-Order Ranking
Dart
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv A predictive quantitative structure`property relationships (QSPR) is developed for modeling the retention index measured on the OV-101 glass capillary gas chromatography column, in a set of 1208 flavor and fragrance compounds. The 4885 molecular descriptors are calculated using the Dragon software and then are simultaneously analyzed through multivariable linear regression analysis using the replacement method (RM) variable subset selection technique. We proceed in three steps, the first one by considering all descriptor blocks, the second one by excluding conformational descriptors blocks, and the last one by analyzing only 3D-descriptors families. The models are properly validated through an external test set of compounds. Cross-validation methods such as leave-one-out and leave-more-out are applied, togetherwith Y-randomization and applicability domain analysis. The results clearly suggest that 3D-descriptors do not offer relevant information for modeling the retention index, while a topological index such as the solvation connectivity index of first order has a high relevance for this purpose.
Fil: Rojas, Cristian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico la Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina
Fil: Pis Diez, Reinaldo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina
Fil: Tripaldi, Piercosimo. Universidad del Azuay; Ecuador
Fil: Duchowicz, Pablo Román. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico La Plata. Centro de Química Inorgánica; Argentina
description A predictive quantitative structure`property relationships (QSPR) is developed for modeling the retention index measured on the OV-101 glass capillary gas chromatography column, in a set of 1208 flavor and fragrance compounds. The 4885 molecular descriptors are calculated using the Dragon software and then are simultaneously analyzed through multivariable linear regression analysis using the replacement method (RM) variable subset selection technique. We proceed in three steps, the first one by considering all descriptor blocks, the second one by excluding conformational descriptors blocks, and the last one by analyzing only 3D-descriptors families. The models are properly validated through an external test set of compounds. Cross-validation methods such as leave-one-out and leave-more-out are applied, togetherwith Y-randomization and applicability domain analysis. The results clearly suggest that 3D-descriptors do not offer relevant information for modeling the retention index, while a topological index such as the solvation connectivity index of first order has a high relevance for this purpose.
publishDate 2015
dc.date.none.fl_str_mv 2015-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/7556
Rojas, Cristian; Pis Diez, Reinaldo; Tripaldi, Piercosimo; Duchowicz, Pablo Román; QSPR analysis for the retention index of flavors and fragrances on a OV-101 column; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 140; 1-2015; 126-132
0169-7439
url http://hdl.handle.net/11336/7556
identifier_str_mv Rojas, Cristian; Pis Diez, Reinaldo; Tripaldi, Piercosimo; Duchowicz, Pablo Román; QSPR analysis for the retention index of flavors and fragrances on a OV-101 column; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 140; 1-2015; 126-132
0169-7439
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0169743914002366
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.chemolab.2014.09.020
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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