Solvent Strength and Selectivity on a Porous Graphitized Carbon Column Separated by a Spectral Mapping Technique Using Barbiturates as Solutes

Abstract

The development and commercialization of automated highspeed instruments has considerably increased the data output in many fields of chromatography. The evaluation of large data matrices, including a high number of variables (i.e. retention time, column temperature, eluent composition, etc.) by the traditional linear regression model, is impossible. During the last few decades, the acceptance and application of various multivariate mathematical statistical methods for this purpose have been among the major advances in the theory and practice of chromatography.1,2 Many multivariate methods have found applications in chromatography, such as linear disriminant analysis, 3 factor analysis, 4 canonical correlation analysis 5 and principal component analysis. 6 The methods mentioned above classify the solutes and chromatographic systems (columns and rows of the retention data matrices) in groups while taking into consideration simultaneously the retention strength and selectivity. However, the separation of these two retention characteristics may promote a better understanding of the chromatographic processes facilitating optimization. A spectral mapping technique (SPM) overcomes this difficulty. SPM has been developed for quantitative structure-activity relationship studies to promote the rational design of new pharmaceuticals, 7 and can also be used in chromatography for separating the retention strength and selectivity.This method divides information into two matrices using the logarithm of the original data. 8 The calculation process consecutively substracts the corresponding column-mean and the corresponding rowmean from each (logarithmic) element. The total variance is divided into the variances of row-means, column-means and the so-called interaction term, which can be reproduced on a plot. The dispersion of objects and variables on the plot translates the existing relationship between them. More details on the calculation process can be found in the original publications. 7,8 The first of the two matrices calculated by SPM is a vector containing the so-called "potency" values, which are related either to the retention strength of various solutes on the same column and in the same mobile phase or to the solvent strength using mobile phases of different composition as well as the same column and the same solutes. The potency values are linearly related to the retention strength, but are expressed in arbitrary units. They always show the quantitative measure of the effect. The second matrix (selectivity map) contains information concerning the spectra of activity (the qualitative characteristics of the effect), that is a selectivity map of the retention data matrix shows either the selectivity of the retention of various solutes on the same column using the same mobile phase or the selectivity of the mobile phases of different compositions using the same column and the same set of solutes. The retention strength and solvent strength as well as the selectivity of solutes and that of mobile phases can be calculate...

Simultaneous effect of organic modifier and physicochemical parameters of barbiturates on their retention on a narrow-bore PGC column

“…The marked impact of molar volume [12], hydrophobicity [13], and electrostatic interactive forces [14] has been demonstrated. In order to increase the reliability of QSRS, linear correlations are frequently calculated between the retention parameter (retention time, capacity factor (k) or logarithm of capacity factor) selected and the parameters of the equation (slope and/or intercept values) are used for the elucidation of the quantitative relationship between retention and solute structure.…”