• Title of article

    Quantitative structure–retention relationships XIV: Prediction of gas chromatographic retention indices for saturated O-, N-, and S-heterocyclic compounds

  • Author/Authors

    Farkas، نويسنده , , Orsolya and Héberger، نويسنده , , Kلroly and Zenkevich، نويسنده , , Igor G.، نويسنده ,

  • Issue Information
    دوفصلنامه با شماره پیاپی سال 2004
  • Pages
    12
  • From page
    173
  • To page
    184
  • Abstract
    In this study, quantitative structure–retention relationship (QSRR) technique was used to find the best approximation and to predict gas chromatographic retention indices for O-, N-, and S-heterocyclic compounds on standard nonpolar polydimethyl siloxane stationary phase. Boiling point (BP) and calculated properties were used to encode the structure of compounds. Three- and two-dimensional calculated properties such as weighted–holistic invariant molecular (WHIM) descriptors, geometry topology and atom weights assembly (GETAWAY) descriptors, connectivity indices, and zero-dimensional constitutive descriptors were used. Variable subset selection (VSS) and partial least squares (PLS) projections to latent structures were used to select the most significant variables from a large set of descriptors. Multiple linear regression (MLR) and PLS were applied to find the relationship between selected properties and gas chromatographic retention indices. PLS was not able to select the most important descriptors (boiling point or molecular weight). The predictive ability of the models was tested by cross-validation. Solely calculated descriptors were not able to give proper models. Boiling point was always necessary for good prediction. PLS models containing boiling points were suitable for retention index prediction, whereas MLR did not give real linear models.
  • Keywords
    variable selection , MLR , PLS , Prediction , Kovلts retention index , Saturated heterocyclic compounds , Gas chromatography
  • Journal title
    Chemometrics and Intelligent Laboratory Systems
  • Serial Year
    2004
  • Journal title
    Chemometrics and Intelligent Laboratory Systems
  • Record number

    1461220