SciELO - Scientific Electronic Library Online

 
vol.65 número4Novel and Reusable Mesoporous Silica Supported 4-Methylbenzenesul- fonate-functionalized Ionic Liquids for Room Temperature Highly Efficient Preparation of 2,4,5-Triaryl-1H-imidazolesImprovement the Physico-chemical Characteristics of Diesel Fuel using Gamma Irradiation índice de autoresíndice de assuntospesquisa de artigos
Home Pagelista alfabética de periódicos  

Serviços Personalizados

Journal

Artigo

Indicadores

Links relacionados

  • Não possue artigos similaresSimilares em SciELO

Compartilhar


Journal of the Mexican Chemical Society

versão impressa ISSN 1870-249X

Resumo

ČABARKAPA, Ivana; AćIMOVIć, Milica; PEZO, Lato  e  TADIć, Vanja. A Validation Model for Prediction of Kovats Retention Indices of Compounds Isolated from Origanum spp. and Thymus spp. Essential Oils. J. Mex. Chem. Soc [online]. 2021, vol.65, n.4, pp.550-559.  Epub 28-Fev-2022. ISSN 1870-249X.  https://doi.org/10.29356/jmcs.v65i4.1515.

This work aimed to obtain a validated model for the prediction of retention times of compounds isolated from Origanum heracleoticum, Origanum vulgare, Thymus vulgaris, and Thymus serpyllum essential oils. In total 68 experimentally obtained retention times of compounds, which were separated and detected by GC-MS were further used to build the prediction models. The quantitative structure-retention relationship was employed to foresee the Kovats retention indices of compounds acquired by GC-MS analysis, using eight molecular descriptors selected by a genetic algorithm. The chosen descriptors were used as inputs for the four artificial neural networks, to construct a Kovats retention indices predictive quantitative structure-retention relationship model. The coefficients of determination in the training cycle were 0.830; 0.852; 0.922 and 0.815 (for compounds found in O. heracleoticum, O. vulgare, T. vulgaris and T. serpyllum essential oils, respectively), demonstrating that these models could be used for prediction of Kovats retention indices, due to low prediction error and high r 2.

Palavras-chave : Origanum spp.; Thymus spp; QSRR; artificial neural networks.

        · resumo em Espanhol     · texto em Inglês     · Inglês ( pdf )