Gregor Reich

Determination of Cu, Fe, Mn, and Zn in blood fractions by SEC-HPLC-ICP-AES coupling.

The binding of Cu, Fe, Mn, and Zn to proteins in blood and in blood fractions was investigated, since their interactions in free radical metabolism in humans is of great interest. An HPLC-ICP-AES technique was developed allowing adequate separation of metalloproteins and of inorganic and organic metal species. For the separation of metalloproteins in erythrocytes and blood plasma a Merck Superformance Fractogel EMD BioSEC 650 (S) column was used. Size exclusion chromatography (SEC)-HPLC was hyphenated to ICP-AES both on-line and off-line for the detection of trace elements in the fractions resulting from HPLC separations. HPLC parameters, pH, temperature, flow rate and salt concentration were optimized for the protein separation and the optimal conditions were applied for the hyphenation to the ICP-AES detector. The separation column was calibrated with five standard proteins. For the element determination by ICP-AES a line selection with respect to the sensitivity was performed. Three different methods were used for the determination of trace elements in blood: direct determinations, on-line and off-line SEC-HPLC-ICP-AES measurements. For the optimizing experiments blood samples of one female subject were used. The direct determination by ICP-AES of the elements was performed in blood and blood fractions of ten different subjects to obtain the average concentration ranges. From the results the identification of the protein Cu/Zn superoxide dismutase in erythrocytes was possible. The LOD were 0.03 microgram mL-1 for Cu, 0.026 microgram mL-1 for Fe, 0.8 ng mL-1 for Mn, and 0.09 microgram mL-1 for Zn in a synthetic blood matrix.

Quantitation of the information content of multi-dimensional gas chromatography and low-resolution mass spectrometry in the identification of doping drugs☆

On the basis of information theory, a comparison of the performance of the identification of compounds by multi-dimensional gas chromatography and binary-encoded low-resolution mass spectrometry is made for doping drugs. This group of compounds was not selected at random but according to their pharmocological effects. For these compounds it was observed that the m/e values of the peaks in the mass spectra are highly correlated, while a number of chromatographic columns can be found on which the retention data are only slightly correlated. As a result, the information content of two-dimensional gas chromatographic retention data on such columns equals the information content of binary-encoded low-resolution mass spectra. It is shown that the accuracy and precision of retention data in gas--liquid chromatography, and therefore the information content, can be significantly improved if the data are corrected for adsorption effects of the solid support and extrapolated to a limiting value in the case of asymmetric peaks.

Characterization and selection of stationary phases for gas-liquid chromatography by pattern recognition methods

Abstract The classification of stationary phases in gas—liquid chromatography and the quantitation of their retention characteristics, which is generally described as ‘polarity’, were investigated by pattern recognition methods, especially by the hierarchical clustering and the minimum spanning tree techniques. It is demonstrated that the hierarchical clustering with a distant function and the minimum spanning tree method gives similar results with respect to the ranking and the differentiation of liquid stationary phases. New measures of the retention characteristics of liquid stationary phases were defined and tested. The potential of the various measures of solvent polarity is discussed. The mean retention index was found to be the best polarity characteristic. With a view to the rationalization of experimental work, an optimized procedure for the classification of liquid stationary phases and the calculation of their polarity with the minimum number of characteristic test solutes was elaborated. The set of characteristic solutes selected gives the best representation of the total number of solutes and covers all types of molecular interactions included in the term polarity.

Recognizing chromatographic peaks with pattern recognition methods : Part 2. Evaluation of different distance measures

Abstract The proposed method for the recognition of peaks in a chromatogram is based on the k-nearest neighbour (KNN) algorithm, in which the chromatogram is compared with a predefined peak profile. A critical part of the KNN algorithm is the selection of the distance measure used to calculate the similarity between points in the pattern space. Here, five different distance functions are evaluated. In addition to the euclidian distance, which was used in the initial tests of the algorithm, the correlation coefficient, the cross-correlation, and the angle between two vectors and its cosine are tested. All these distance measures show satisfactory behaviour. Their different characteristics are discussed.

Recognizing chromatographic peaks with pattern recognition methods : Part 1. Development of a k-nearest-neighbour technique

Abstract Problems in automated peak recognition in chromatography are discussed. An algorithm based on the k -nearest neighbour technique is proposed. Recognition of a peak is done by comparing it with a predefined profile function (normally a Gaussian peak profile). The profile and a part of the chromatogram are both interpreted as points in a multi-dimensional pattern space. The distance between the two points gives the value of the peak recognition function. The effects of different properties of chromatographic peaks (i.e., peak width, peak height and noise) and of the profile parameter (i.e., dimension of the pattern space, shape and width of the function, and characteristics of the distance measure) are evaluated. The method has excellent properties for recognizing peaks with low signal/noise ( S / N ) ratios; an example with S / N = 1 is shown. Changing peak widths and drifting baselines have little effect on the recognition ability. Difficulties with changing peak heights can be compensated by range scaling. Problems occur when two peaks are not sufficiently separated.

Extraction of information on the chemical structure of monofunctional compounds from retention data in gas—liquid chromatography by pattern recognition methods

Abstract A method for the determination of structural features of unknown compounds, based on multi-dimensional gas—liquid Chromatographic retention data, is presented. The species investigated are monofunctional compounds. The retention index is used as retention parameter. A two-step classification procedure is reported. The first step is the determination of a correction parameter for the retention index, the skeleton number. With these correction terms, the retention data are modified, and the second classification step for the determination of functional groups is executed with the modified data. The best classification obtained is based on the linear learning machine method. A 10-dimensional data set, i.e. the use of 10 stationary liquids, is sufficient for the total classification.

Prediction of partition coefficients in ternary liquid-liquid systems as a function of the phase system composition by factor analysis for

Abstract The measured partition data of 26 steroids for six different compositions of ternary liquid-liquid systems consisting of 2,2,4-trimethylpentane, ethanol and water are used in calculations of partition data for new phase system compositions. It was possible to reproduce the original data and to verify new experimental data by this calculation with high accuracy. Calculations were done in three ways. In the first, non-linear regression of the partition coefficients as a function of the phase system composition was examined. With this classical approach it is possible to calculate the values of the partition coefficients, solute by solute, with good accuracy. In the second approach, principal component factor analysis was used. The phase system-specific factors for further phase system compositions were calculated with a non-linear regression of these factors on the phase system composition. In combination with the solute-specific factors, the values of the partition coefficients for all solutes in any phase system composition could be predicted with excellent accuracy. In the third approach, target transformation factor analysis was applied. A relationship between the partition coefficient and the phase system composition was established on the basis of either the phase system composition itself or a simple function of it. This method also gave very accurate predictions of known and unknown partition coefficients.

SIMILARITY OF BINARY MIXED AQUEOUS-ORGANIC SOLVENTS FOR CAPILLARY ZONE ELECTROPHORESIS BASED ON THE IONIC MOBILITY OF SUBSTITUTED BENZOATES EVALUATED BY CLUSTER ANALYSIS AND INFORMATION THEORY

The similarity of solvents used for buffering background electrolytes in capillary zone electrophoresis is evaluated by two chemometric techniques: cluster analysis and information theory. The solvents are water and binary aqueous-organic mixtures of methanol, ethanol, 1-propanol, and acetonitrile, respectively, with concentrations up to 80% vrv of the organic constituent. The electrophoretic properties of the solvents are described by the actual mobilities of 26 aromatic anions, hydroxy-, methyl-, nitro-, and chloro-substituted benzoates. Cluster analysis results in two main clusters, the one consisting of the alcoholic systems, the other of water and the mixtures with acetonitrile. The information content of the single solvent systems ranges up to 4 bit for the acetonitrilic solvent. The similarity of the systems is evaluated by information theory from the loss of information content due to the correlation between the mobility data in the particular solvents. Q 1999 John Wiley & Sons, Inc. J Micro Sep 11: 576)581, 1999