Prediction of GC Retention Indexes for Insect-Produced Methyl-Substituted Alkanes Using an Artificial Neural Network and Simple Structural Descriptors

Logical criteria for estimating gas chromatographic retention indices of organic compounds using their physicochemical properties

There is no unified equation that would correlate gas chromatographic retention indices of any organic compounds with their normal boiling points (at atmospheric pressure). Nevertheless, RI estimation with acceptable accuracy is possible using principal physicochemical properties, namely, boiling points, relative densities, and indices of refraction. The main feature of the algorithms proposed is the application of a logical criteria for comparing so-called indices of boiling points I(Tb), molecular weights I(M), and molar refractions I(MRD). If the comparison of these indices confirms the approximate equality of any two or all three of them (that is typical for nonpolar organic compounds), then as the best estimate of GC retention indices (RIcalc) we can accept I(Tb) values without any additional calculations. Such comparison is formally equivalent to the application of logical criteria for I(Tb), I(M), and I(MRD) values. For polar analytes, the relation I(Tb) > I(MRD) > I(M) is valid, and in these cases the inequality I(Tb) > RIcalc > I(M) appeared to be correct. Instead of large intervals between I(Tb) and I(M) for estimating RIcalc, the use of the relation RIcalc ≈ (1 - a)I(Tb) + aI(MRD) + b is proposed. The indices of molecular weights are not used for the calculations directly, but they are needed just for application of the logical criteria. The average accuracy of RI estimation by the method proposed is 9-16 index units for nonpolar compounds of any chemical nature and up to 27 for polar analytes. This high accuracy is comparable to that of the more sophisticated modern algorithms. The restriction of this approach is the impossibility of applying it to poly- and perfluoroorganic compounds.

Quantitative structure-(chromatographic) retention relationships

Since the pioneering works of Kaliszan (R. Kaliszan, Quantitative Structure-Chromatographic Retention Relationships, Wiley, New York, 1987; and R. Kaliszan, Structure and Retention in Chromatography. A Chemometric Approach, Harwood Academic, Amsterdam, 1997) no comprehensive summary is available in the field. Present review covers the period of 1996-August 2006. The sources are grouped according to the special properties of kinds of chromatography: Quantitative structure-retention relationship in gas chromatography, in planar chromatography, in column liquid chromatography, in micellar liquid chromatography, affinity chromatography and quantitative structure enantioselective retention relationships. General tendencies, misleading practice and conclusions, validation of the models, suggestions for future works are summarized for each sub-field. Some straightforward applications are emphasized but standard ones. The sources and the model compounds, descriptors, predicted retention data, modeling methods and indicators of their performance, validation of models, and stationary phases are collected in the tables. Some important conclusions are: Not all physicochemical descriptors correlate with the retention data strongly; the heat of formation is not related to the chromatographic retention. It is not appropriate to give the errors of Kovats indices in percentages. The apparently low values (1-3%) can disorient the reviewers and readers. Contemporary mean interlaboratory reproducibility of Kovats indices are about 5-10 i.u. for standard non polar phases and 10-25 i.u. for standard polar phases. The predictive performance of QSRR models deteriorates as the polarity of GC stationary phase increases. The correlation coefficient alone is not a particularly good indicator for the model performance. Residuals are more useful than plots of measured and calculated values. There is no need to give the retention data in a form of an equation if the numbers of compounds are small. The domain of model applicability of models should be given in all cases.

Quantitative Structure-Property Relationship Study of the Solvent Polarity Using Wavelet Neural Networks

Quantitative structure-property relationship (QSPR) studies based on artificial neural network (ANN) and wavelet neural network (WNN) techniques were carried out for the prediction of solvent polarity. Experimental S' values for 69 solvents were assembled. This set included saturated and unsaturated hydrocarbons, solvents containing halogen, cyano, nitro, amide, sulfide, mercapto, sulfone, phosphate, ester, ether, etc. Semi-empirical quantum chemical calculations at AM1 level were used to find the optimum 3D geometry of the studied molecules and different quantum-chemical descriptors were calculated by the HyperChem software. A stepwise MLR method was used to select the best descriptors and the selected descriptors were used as input neurons in neural network models. The results obtained by the two methods were compared and it was shown that in WNN, the convergence speed was faster and the root mean square error of prediction set was also smaller than ANN. The average relative error in WNN was 7.9 and 6.8% for calibration and prediction set, respectively, and the results showed the ability of the WNN developed here to predict solvent polarity.