Biomedical subjects

K Héberger

Publications and source records attributed to K Héberger.

6 recordsLinked to original sources

PubMed2002

Differentiation of vegetable oils by mass spectrometry combined with statistical analysis.

The main triacylglycerol (TAG) composition of different plant oils (almond, avocado, corn germ, grape seed, linseed, mustard seed, olive, peanut, pumpkin seed, sesame seed, soybean, sunflower, walnut and wheat germ) were analyzed using two different mass spectrometric techniques: HPLC/APCI-MS (high-performance liquid chromatography/atmospheric pressure chemical ionization mass spectrometry) and MALDI-TOFMS (matrix-assisted laser desorption/ionization time-of-flight mass spectrometry).Linear discriminant analysis (LDA) as a multivariate mathematical statistical method was successfully used to distinguish different plant oils based on their relative TAG composition. With LDA analysis of either APCI-MS or MALDI-MS data, the classification among the almond, avocado, grape seed, linseed, mustard seed, olive, sesame seed and soybean oil samples was 100% correct. In both cases only 6 different oil samples from a total of 73 were not classified correctly.

Chromatography, High Pressure Liquid↗

PubMed2002

Variable selection using pair-correlation method. Environmental applications.

Pair-correlation method (PCM) has been developed for selecting between two, correlated descriptor variables. PCM utilizes systematic information present in the scatter of QSAR applications. The data are suitably ordered in a 2 x 2 contingency table. Statistical tests are used to discriminate between the descriptor variables. We have developed, adapted, investigated and compared the following test statistics to each other: Conditional Fisher's exact test (CE), McNemar's test (MN), Chi-square test and Williams' t-test (Wt). If a test indicates significant difference between the descriptors, we use the terms superior-inferior or winner-loser for the overwhelming and subordinate descriptors, respectively. If more than two variables are to be compared, the discrimination can be made pair-wise and then the variables have to be ordered. Three ways of ordering have been used: simple ordering (number of wins), ordering according to the differences between wins and losses, and ordering according to probability-weighted differences between wins and losses. The basic algorithm of PCM has been described in this paper, the various selection criteria and the ordering methods were compared on suitable model systems. These case studies involved description of cAMP phospodiesterase inhibition by flavons, toxicity of chlorobenzenes and mutagenic character of aromatic and heteroaromatic amines.

3',5'-Cyclic-AMP Phosphodiesterases↗

PubMed2002

Prediction of tumoricidal activity and accumulation of photosensitizers in photodynamic therapy using multiple linear regression and artificial neural networks.

The biological activities of a congeneric series of pyropheophorbides used as sensitizers in photodynamic therapy have been predicted on the basis of their molecular structures, using multiple linear regression and artificial neural network (ANN) computations. Theoretical descriptors (a total of 81) were calculated by the 3DNET program based on the three-dimensional structure (3D) of the geometry-optimized molecules. These input descriptors were tested as independent variables and used for model building. Systematic descriptor selections yielded models with one, two or three descriptors with good cross-validation results. The predictive abilities of the best fitting models were checked by shuffling and cross-validation procedures. ANN was suitable for building models for both linear and nonlinear relationships. Lipophilicity was sufficient to predict the accumulation of the sensitizers in the target tissue. Weighted holistic invariant molecular descriptors weighted by atomic mass, Van der Waals volume or electronegativity were also needed to predict photodynamic activity properly. Our models were able to predict the biological activities of 13 pyropheophorbide derivatives solely on the basis of their 3D molecular structures. Moreover, linear and nonlinear variable selection methods were compared in models built linearly and nonlinearly. It is expedient to use the same method (linear or nonlinear) for variable selection as for parameter estimation.

Antineoplastic Agents↗

PubMed2001

Estimation of molar heat capacities in solution from gas chromatographic data.

The temperature dependence of retention data (retention or capacity factors) is measured for 35 aliphatic ketones and aldehydes as model compounds on a dimethylpolysiloxane stationary phase. A novel model is derived to determine the heat of solution and the solution molar heat capacities from the fits of the log natural of the difference of the retention factor and the column temperature (T) versus 1/T and the temperature arrangement. The convex curvature present in the residual plots of a former defined equation of ours disappears when applying a newly defined model. A detailed statistical analysis clearly shows the superiority of the refined model to the earlier one in a broader temperature range. The validation of this model is made through a comparison of heat capacity values taken from literature determined by different methods. The molar heat capacity of a pure liquid oxo compound is similar to that of when the same compound is solvated in a stationary phase.

Journal Article↗

PubMed2001

Principal component analysis of polarity and interaction parameters in inverse gas chromatography.

Inverse gas chromatography is used in the characterization of aliphatic-aromatic and aromatic ketones, their oximes, and ketone-oxime or oxime-oxime mixtures. All these organic materials are used as liquid stationary phases in gas chromatographic columns. A series of polarity and Flory-Huggins interaction parameters are determined and used to describe the physicochemical properties of examined materials, metal extractants, and products of their degradation. Principal component analysis (PCA) is performed on a data matrix consisting of polarity and interaction parameters for ketones, their oximes, and mixtures. The calculations are carried out on the correlation matrix. It is found that seven principal components account for more than 95% of the total variance in the data, indicating that the polarity (interaction) parameters are not correlating well. Physical meanings are attributed to the principal components, the most influential ones being that the first and the second principal components account for several Flory-Huggins interaction parameters, whereas the fifth is correlated with criterion "A". The plots of component loadings show characteristic groupings of polarity indicators, whereas that of component scores show several groupings of stationary phases. Cluster analysis provides mainly the same groupings. PCA allows for the grouping of polarity and solubility parameters based on the information carried within those parameters. There is no need to use more than one parameter from each cluster. McReynolds polarity and the partial molar excess Gibbs free energy of solution per methylene group carry the same information. The groups of ketones, oximes, and their mixtures can be distinguished with the use of PCA on the basis of the measured polarity, solubility parameters, or both.

Chemical Phenomena↗

PubMed1999

Principal component analysis of measured quantities during degradation of hydroperoxides in oxidized vegetable oils.

Decomposition of hydroperoxides in sunflower oil under strictly oxygen-free conditions was followed by measuring peroxide values against time, absorbance values at 232 and 268 nm, para-anisidine values, and by quantitative analyses of volatile products using various additives. The results were arranged in a matrix form and subjected to principal component analysis. Three principal components explained 89-97% of the total variance in the data. The measured quantities and the effect of additives were closely related. Characteristic plots showed similarities among the measured quantities (loading plots) and among the additives (score plots). Initial decomposition rate of hydroperoxides and the amount of volatile products formed were similar to each other. The outliers, the absorbance values, were similar to each other but carried independent information from the other quantities. Para-anisidine value (PAV) was a unique parameter. Since PAV behaved differently during the course of hydroperoxide degradation, it served as a kinetic indicator. Most additives were similar in their effects on the mentioned quantities, but two outliers were also observed. Rotation of the principal component axes did not change the dominant patterns observed. The investigations clearly showed which variables were worth measuring to evaluate different additives.

Analysis of Variance↗