Retention index, molecular

J.R.M. Smits, W.J. Meissen, G.J. Daalmans and G. Kateman, Using molecular representations in combination with neural networks. A case study prediction of the HPLC retention index. Computers Chem., 18 (1994) 157-172. 

The HcReynolds abroach, which was based on earlier theoretical considerations proposed by Rohrschneider, is formulated on the assumption that intermolecular forces are additive and their Individual contributions to retention can be evaluated from differences between the retention index values for a series of test solutes measured on the liquid phase to be characterized and squalane at a fixed temperature of 120 C. The test solutes. Table 2.12, were selected to express dominant Intermolecular interactions. HcReynolds suggested that ten solutes were needed for this purpose. This included the original five test solutes proposed by Rohrschneider or higher molecular weight homologs of those test solutes to improve the accuracy of the retention index measurements. The number of test solutes required to adequately characterize the solvent properties of a stationary phase has remained controversial but in conventional practice the first five solutes in Table 2.12, identified by symbols x through s have been the most widely used [6). It was further assumed that for each type of intermolecular interaction, the interaction energy is proportional to a value a, b, c, d, or e, etc., characteristic of each test solute and proportional to its susceptibility for a particular interaction, and to a value x, X, Z, U, s, etc., characteristic of the capacity of the liquid phase... 

A set of n = 209 polycyclic aromatic compounds (PAC) was used in this example. The chemical structures have been drawn manually by a structure editor software approximate 3D-structures including all H-atoms have been made by software Corina (Corina 2004), and software Dragon, version 5.3 (Dragon 2004), has been applied to compute 1630 molecular descriptors. These descriptors cover a great diversity of chemical structures and therefore many descriptors are irrelevant for a selected class of compounds as the PACs in this example. By a simple variable selection, descriptors which are constant or almost constant (all but a maximum of five values constant), and descriptors with a correlation coefficient >0.95 to another descriptor have been eliminated. The resulting m = 467 descriptors have been used as x-variables. The y-variable to be modeled is the Lee retention index (Lee et al. 1979) which is based on the reference values 200, 300, 400, and 500 for the compounds naphthalene, phenanthrene, chrysene, and picene, respectively. 

Liu, S., Yin, C., Cai, S., Li, Z. Chemom. Intell. Lab. Syst. 61, 2002, 3-15. Molecular structural vector description and retention index of polycyclic aromatic hydrocarbons. 

The aids to chromatography include a) resolution calculations on chromatograms of standard mixtures to monitor column performance, b) calculation of Kovats retention index for help in identifying peaks, and (c) multiple point calibration curves for improved quantitation. The file searching routines access two sets of data. Information (such as molecular formula, molecular weight) is stored on 3100 compounds from the Arctander data( ). This allows a quick computer search through the data which is difficult... 

The molecular weight and the Kovats retention index can then be combined to aid in the identification of the component. A linear relationship exists between the molecular weight and retention index for a homologous series of compounds. The relationship varies for each class of compound thus, a clue can be obtained regarding the type of compound present which can be verified by some other technique. 

In Figure 3, a straight-line relationship between a retention index and molecular weight is illustrated for homologous series of n-alcohols, n-aldehydes, n-acetates, and n-hydrocarbons. The compound having molecular weight 130 and retention index 1180 corresponds to n-pentyl acetate. Since the relationship in Figure 3 holds for all types of related compounds, it is obvious that this technique could be widely used in identification of GC peaks. 

Figure 3. Relationship between molecular weight and retention index...

Table I. Identification of Unknown Mixture Using Both Molecular Weight and Retention Index...

Solute Melting Point (°C) Fused Ring Number Molecular Connectivity (x) k(SFC) [C18] Retention Index OV-101... 

A dimensionless molecular retention index Me parameter can be defined as the sum of Mr (relative molecular weight) and a structural increment W. Contained in W are all the additive contributions of the functional groups (see Eq. 4-52) which differ from a hypothetical n-alkane with the same Mr value. According to definition, the W values of the n-alkanes are always equal to zero. In this manner it is possible to estimate the partition coefficients of any given organic compound between a gas and any given liquid or polymer with help of additive structural increments. 

From the definition of the molecular retention index Me(i) for a substance i (Pirin-ger et al., 1976) ... 

The increase in the activity coefficient overides the decrease in the partial pressure p i). For this reason one can define a molecular retention index -t) for such systems using the following equation ... 

TTie structural features are represented by molecular descriptors, which are numeric quantities related directly to the molecular structure rather than physicochemical properties. Examples of such descriptors include molecular weight, molecular connectivity indexes, molecular complexity (degree of substitution), atom counts and valencies, charge, molecular polarizability, moments of inertia, and surface area and volume. Once a set of descriptors has been developed and tested to remove interdependent/collinear variables, a linear regression equation is developed to correlate these variables with the retention parameter of interest, e.g., retention index, retention volume, or partition coefficient The final equation includes only those descriptors that ate statistically significant and provide the best fit to the data. For more details on QSRR and the development and use of molecular descriptors, the reader is referred to the literature [188,195,198,200-202 and references therein]. 

Gautzsch, R. and Zinn, P. (1994). List Operations on Chemical Graphs. 5. Implementation of Breadth-First Molecular Path Generation and Application in the Estimation of Retention Index Data and Boiling Points. J.Chem.lnf.Comput.ScL,34,791-800. 

Kier, L.B. and Hall, L.H. (1979). Molecular Connectivity Analyses of Structure Influencing Chromatographic Retention Indexes. J.Pharm.ScL, 68,120-122. 

Klappa, S.A. and Long, G.R. (1992). Computer Assisted Determination of the Biological Activity of Polychlorinated Biphenyls Using Gas Chromatographic Retention Indexes as Molecular Descriptors. Anal.Chim.Acta, 259,89-93. 

Robbat Jr., A., Corso, N.P., Doherty, P.J. and Marshall, D. (1986a). Multivariate Relationships Between Gas Chromatographic Retention Index and Molecular Connectivity of Mononitrated Polycyclic Aromatic Hydrocarbons. Anal.Chem., 58,2072-2077. 

Rohrbaugh, R.H. and Jurs, PC. (1987b). Molecular Shape and the Prediction of High-Performance Liquid Chromatographic Retention Indexes of Polycyclic Aromatic Hydrocarbons. AnaiChem., 59,1048-1054. 

Rohrbaugh, R.H. and Jurs, P.C. (1987b) Molecular shape and the prediction of high-performance liquid chromatographic retention indexes of polycyclic aromatic hydrocarbons. Anal. Chem.,... 

R. H. Rohrbaugh and P. C.. lurs. Anal. Chem., 59, 1048 (1987). Molecular Shape and the Prediction of High-Performance Liquid Chromatographic Retention Indexes of Polycyclic Aromatic Hydrocarbons. See also, P. C. Jurs, in Reviews in Computational Chemistry, K. B. l.ip-kowitz and D. B. Boyd, Eds., VCH Publishers, New York, 1990, pp. 169-212. Chemomctrics and. Multivariate Analysis in Analytical Chemistry. 

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