Characterization and prediction of retention behavior in reversed-phase chromatography using factor analytical modeling

Lochmüller, C. H.; Breiner, Steven J.; Reese, Charles E.; Koel, Marlies

doi:10.1021/ac00179a016

Cited by 21 publications

(3 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lochmüller et al . applied multivariate statistical techniques of principal component analysis and target transformation factor analysis to examine the reversed-phase high-performance liquid chromatography behavior of some 35 benzene derivatives in different solvent systems. Partition coefficient data for 50 solutes in 6 nonpolar solvent systems on analysis by PCA showed that the relationship between solute structure and partitioning behavior for simple organic compounds depended on the isotropic surface area as the most important parameter.…”

Section: Introductionmentioning

confidence: 99%

The Classification of Solvents by Combining Classical QSPR Methodology with Principal Component Analysis

et al. 2005

View full text Add to dashboard Cite

The results of a quantitative structure-property relationship (QSPR) analysis of 127 different solvent scales and 774 solvents using the CODESSA PRO program are presented. QSPR models for each scale were constructed using only theoretical descriptors. The high quality of the models is reflected by the squared multiple correlation coefficients that range from 0.726 to 0.999; only 18 models have R2< 0.800. This enables direct theoretical calculation of predicted values for any scale and/or for any organic solvent, including those previously unmeasured. The molecular descriptors involved in the models are classified and discussed according to (i) the origin of their calculation (i.e., constitutional, geometric, charge-related, etc.) and (ii) the commonly accepted classification of physical interactions between the solute and solvent molecules in liquid (condensed) media. A reduced matrix 774 (solvents) x 100 (solvent scales) was selected for the principal component analysis (PCA) by taking into account only the solvent scales with more than 20 experimental data points. The first 5 principal components account for 75% of the total variance. The robustness of the PCA model obtained was validated by the comparison models development for restricted submatrices of data and with the results obtained for the full data set. The total variance accounted for by the first three PCs, for the submatrices with the same number of solvent scales but different numbers of solvents, varies from 68.2% to 59.0%. This demonstrates that the total variance described by the first 3 components is essentially stable as the number of solvents involved varies from 100 to 774. Subsequently, a matrix with 703 diverse solvents and 100 solvent scales was selected for the general classification of the solvents and scales according to the scores and loadings obtained from the PCA treatment. Classification of the theoretical molecular descriptors, derived from the chemical structure alone, according to their relevance to specific types of intermolecular interaction (cavity formation, electrostatic polarization, dispersion, and hydrogen bonding) in liquid media enables a more easily comprehensible physical interpretation of the QSPR of molecular properties in liquids and solutions. The reported QSPR models for solvent scales with theoretical molecular descriptors and the results of the PCA analysis are potentially of great practical importance, as they extend the applicability of correlations with empirical solvent scales to many previously unmeasured systems.

show abstract

Section: Introductionmentioning

confidence: 99%

The Classification of Solvents by Combining Classical QSPR Methodology with Principal Component Analysis

et al. 2005

View full text Add to dashboard Cite

show abstract

“…In this review paper three strategies are mentioned: True physical models (1) including the polarity scale and correlation with solvatochromic parameters, (2) methods which assume no model, and (3) methods with an abstract or hidden model. Lochmüller et al [17] utilized a prediction strategy in an abstract multiple linear model with logarithmic retention factors of four training solutes. In this procedure the chromatographic system is characterized by four loading factors from which retention factors can be calculated with a mean error of 6.1%.…”

Section: Introductionmentioning

confidence: 99%

Prototypical Test Substances for a Reversed Phase in a Retention Parameter Model

Rohrschneider¹

1999

J. High Resol. Chromatogr.

View full text Add to dashboard Cite

“…Orthogonality is usually investigated in normalized space defined as a rectangular surface delimited by the first and last retained compound in each dimension, and it may be evaluated mainly through the measure of (1) independence of dimensions, meaning divergence of retention mechanisms through visual comparison of projections of the retention in Cartesian space, − correlation tests, ,,− regression trees, , factor analysis, ,, PCA − ,− or HCA, ,,,, measure of spreading angle, and through the information theory or divergence of stationary phases’ descriptors; ,− (2) spatial spreading of peaks by information theory ,,,,− or system dimensionality; (3) peak capacity; ,,, as well as (4) the part occupied by peaks in separation space (often expressed as percentage). ,,,− …”

mentioning

confidence: 99%

Assessment of Two-Dimensional Separative Systems Using Nearest-Neighbor Distances Approach. Part 1: Orthogonality Aspects

et al. 2013

View full text Add to dashboard Cite

We propose here a new approach to the evaluation of two-dimensional and, more generally, multidimensional separations based on topological methods. We consider the apex plot as a graph, which could further be treated using a topological tool: the measure of distances between the nearest neighbors (NND). Orthogonality can be thus defined as the quality of peak dispersion in normalized separation space, which is characterized by two factors describing the population of distances between nearest neighbors: the lengths (di(o)) of distances and the degree of similarity of all lengths. Orthogonality grows with the increase of both factors. The NND values were used to calculate a number of new descriptors. They inform about the extent of peak distribution, like the arithmetic mean (A̅(o)) of NNDs, as well as about the homogeneity of peak distribution, like the geometric mean (G̅(o)) and the harmonic mean (H̅(o)). Our new, NND-based approach was compared with another recently published method of orthogonality evaluation: the fractal dimensionality (DF). The comparison shows that the geometric mean (G̅(o)) is the descriptor behaving in the most similar way to dimensionality (DF) and the harmonic mean (H̅(o)) displays superior sensitivity to the shortest, critical distances between peaks. The latter descriptor (H̅(o)) can be considered as sufficient to describe the degree of orthogonality based on NND. The method developed is precise, simple, easy to implement, and possible to use for the description of separations in a true or virtual system of any number of dimensions.

show abstract

Characterization and prediction of retention behavior in reversed-phase chromatography using factor analytical modeling

Cited by 21 publications

References 25 publications

The Classification of Solvents by Combining Classical QSPR Methodology with Principal Component Analysis

The Classification of Solvents by Combining Classical QSPR Methodology with Principal Component Analysis

Prototypical Test Substances for a Reversed Phase in a Retention Parameter Model

Assessment of Two-Dimensional Separative Systems Using Nearest-Neighbor Distances Approach. Part 1: Orthogonality Aspects

Contact Info

Product

Resources

About