dc.creatorRojas Villa, Cristian Xavier
dc.creatorTripaldi, Piercosimo
dc.creatorDuchowicz, Pablo Román
dc.date2016
dc.date2020-11-04T23:32:01Z
dc.date.accessioned2023-07-14T23:13:34Z
dc.date.available2023-07-14T23:13:34Z
dc.identifierhttp://sedici.unlp.edu.ar/handle/10915/108337
dc.identifierissn:2379-7479
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7449835
dc.descriptionThe 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.
dc.descriptionInstituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas
dc.formatapplication/pdf
dc.format78-93
dc.languageen
dc.rightshttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rightsCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.subjectCiencias Exactas
dc.subjectDragon Software
dc.subjectk-Means Cluster Analysis
dc.subjectQSPR Theory
dc.subjectRelative Sweetness
dc.subjectReplacement Method
dc.subjectSweeteners
dc.titleA New QSPR Study on Relative Sweetness
dc.typeArticulo
dc.typeArticulo


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