A New QSPR Study on Relative Sweetness
- Autores
- Rojas Villa, Cristian Xavier; Tripaldi, Piercosimo; Duchowicz, Pablo Román
- Año de publicación
- 2016
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- The aim of this work was to develop predictive structure-property relationships (QSPR) of natural and synthetic sweeteners in order to predict and model relative sweetness (RS). The data set was composed of 233 sweeteners collected from diverse sources in the literature, which was divided into training (163) and test (70) molecules according to a procedure based on k-means cluster analysis. A total of 3763 non-conformational Dragon molecular descriptors were calculated which were simultaneously analyzed through multivariable linear regression analysis coupled with the replacement method variable subset selection technique. The established six-parameter model was validated through the cross-validation techniques, together with Y-randomization and applicability domain analysis. The results for the training set and the test set showed that the non-conformational descriptors offer relevant information for modeling the RS of a compound. Thus, this model can be used to predict the sweetness of both un-evaluated and un-synthesized sweeteners.
Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas - Materia
-
Ciencias Exactas
Dragon Software
k-Means Cluster Analysis
QSPR Theory
Relative Sweetness
Replacement Method
Sweeteners - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/4.0/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/108337
Ver los metadatos del registro completo
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A New QSPR Study on Relative SweetnessRojas Villa, Cristian XavierTripaldi, PiercosimoDuchowicz, Pablo RománCiencias ExactasDragon Softwarek-Means Cluster AnalysisQSPR TheoryRelative SweetnessReplacement MethodSweetenersThe aim of this work was to develop predictive structure-property relationships (QSPR) of natural and synthetic sweeteners in order to predict and model relative sweetness (RS). The data set was composed of 233 sweeteners collected from diverse sources in the literature, which was divided into training (163) and test (70) molecules according to a procedure based on k-means cluster analysis. A total of 3763 non-conformational Dragon molecular descriptors were calculated which were simultaneously analyzed through multivariable linear regression analysis coupled with the replacement method variable subset selection technique. The established six-parameter model was validated through the cross-validation techniques, together with Y-randomization and applicability domain analysis. The results for the training set and the test set showed that the non-conformational descriptors offer relevant information for modeling the RS of a compound. Thus, this model can be used to predict the sweetness of both un-evaluated and un-synthesized sweeteners.Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas2016info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf78-93http://sedici.unlp.edu.ar/handle/10915/108337enginfo:eu-repo/semantics/altIdentifier/issn/2379-7479info:eu-repo/semantics/altIdentifier/doi/10.4018/ijqspr.2016010104info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:24:32Zoai:sedici.unlp.edu.ar:10915/108337Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:24:33.056SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
A New QSPR Study on Relative Sweetness |
title |
A New QSPR Study on Relative Sweetness |
spellingShingle |
A New QSPR Study on Relative Sweetness Rojas Villa, Cristian Xavier Ciencias Exactas Dragon Software k-Means Cluster Analysis QSPR Theory Relative Sweetness Replacement Method Sweeteners |
title_short |
A New QSPR Study on Relative Sweetness |
title_full |
A New QSPR Study on Relative Sweetness |
title_fullStr |
A New QSPR Study on Relative Sweetness |
title_full_unstemmed |
A New QSPR Study on Relative Sweetness |
title_sort |
A New QSPR Study on Relative Sweetness |
dc.creator.none.fl_str_mv |
Rojas Villa, Cristian Xavier Tripaldi, Piercosimo Duchowicz, Pablo Román |
author |
Rojas Villa, Cristian Xavier |
author_facet |
Rojas Villa, Cristian Xavier Tripaldi, Piercosimo Duchowicz, Pablo Román |
author_role |
author |
author2 |
Tripaldi, Piercosimo Duchowicz, Pablo Román |
author2_role |
author author |
dc.subject.none.fl_str_mv |
Ciencias Exactas Dragon Software k-Means Cluster Analysis QSPR Theory Relative Sweetness Replacement Method Sweeteners |
topic |
Ciencias Exactas Dragon Software k-Means Cluster Analysis QSPR Theory Relative Sweetness Replacement Method Sweeteners |
dc.description.none.fl_txt_mv |
The aim of this work was to develop predictive structure-property relationships (QSPR) of natural and synthetic sweeteners in order to predict and model relative sweetness (RS). The data set was composed of 233 sweeteners collected from diverse sources in the literature, which was divided into training (163) and test (70) molecules according to a procedure based on k-means cluster analysis. A total of 3763 non-conformational Dragon molecular descriptors were calculated which were simultaneously analyzed through multivariable linear regression analysis coupled with the replacement method variable subset selection technique. The established six-parameter model was validated through the cross-validation techniques, together with Y-randomization and applicability domain analysis. The results for the training set and the test set showed that the non-conformational descriptors offer relevant information for modeling the RS of a compound. Thus, this model can be used to predict the sweetness of both un-evaluated and un-synthesized sweeteners. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas |
description |
The aim of this work was to develop predictive structure-property relationships (QSPR) of natural and synthetic sweeteners in order to predict and model relative sweetness (RS). The data set was composed of 233 sweeteners collected from diverse sources in the literature, which was divided into training (163) and test (70) molecules according to a procedure based on k-means cluster analysis. A total of 3763 non-conformational Dragon molecular descriptors were calculated which were simultaneously analyzed through multivariable linear regression analysis coupled with the replacement method variable subset selection technique. The established six-parameter model was validated through the cross-validation techniques, together with Y-randomization and applicability domain analysis. The results for the training set and the test set showed that the non-conformational descriptors offer relevant information for modeling the RS of a compound. Thus, this model can be used to predict the sweetness of both un-evaluated and un-synthesized sweeteners. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Articulo 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://sedici.unlp.edu.ar/handle/10915/108337 |
url |
http://sedici.unlp.edu.ar/handle/10915/108337 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/issn/2379-7479 info:eu-repo/semantics/altIdentifier/doi/10.4018/ijqspr.2016010104 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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application/pdf 78-93 |
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