Prediction of gas chromatographic retention indexes of polycyclic aromation compounds and nitrated polycyclic aromatic compounds

On the Relation between Retention Indexes and the Interaction between the Solute and the Column in Gas−Liquid Chromatography

“…With the use of descriptors, retention indexes have been derived for a different type of organic compound groups such as alcohols and olefins, [2][3][4] polychlorinated biphenyls (PCBs), polychlorinated dibenzo-dioxines (PCDDs) and polychlorinated dibenzofuranes (PCDFs), [5][6][7][8][9][10][11] hydrocarbons, 1,[12][13][14][15] and polycyclic aromatic hydrocarbons (PAHs) and nitro-PAHs. 9,[16][17][18][19][20][21][22][23][24][25][26] The descriptors are linearly correlated with multilinear regression to the retention indexes. Because of small prediction capability or colinearity only a handful of the descriptors is useful for predicting retention indexes.…”

“…They have been used together with other descriptors such as polarizability, ionization potential, dipole moment, quadrupole moment, length, width, height, molecular weight, heat of formation, highest occupied molecular orbital (HOMO), lowest unoccupied molecular orbital (LUMO), number of rings, and molecular weight to predict retention indexes. With the use of descriptors, retention indexes have been derived for a different type of organic compound groups such as alcohols and olefins, − polychlorinated biphenyls (PCBs), polychlorinated dibenzodioxines (PCDDs) and polychlorinated dibenzofuranes (PCDFs), − hydrocarbons, ,− and polycyclic aromatic hydrocarbons (PAHs) and nitro-PAHs. ,− The descriptors are linearly correlated with multilinear regression to the retention indexes. Because of small prediction capability or colinearity only a handful of the descriptors is useful for predicting retention indexes.…”

Computer-assisted prediction of gas chromatographic retention times of polychlorinated biphenyls

“…In the present study, we investigated the possibility of predicting the GC retention of PCBs by using information obtained directly from their molecular structures. This approach to the computer-assisted prediction of chromatographic retention has been applied previously to polycyclic aromatic hydrocarbons (17,18). Basically, a regression equation is developed by using measured retention times as the dependent variable and molecular structure descriptors as the predictors (independent variables).…”

Computer-assisted prediction of gas chromatographic retention indexes of pyrazines

“…Such models are termed quantitative structure-retention relationships (QSRR). The derivation of such relationships and the definitions of several typical descriptors have been described by Kaliszan (7) and have been employed in studies related to that reported here (8,9). An advantage of the QSRR approach is that the equations generated allow for the prediction of retention indices for compounds which are structurally similar to those used to develop the model but which were not specifically represented in the training set.…”