Orthogonality and similarity within silica-based reversed-phased chromatographic systems

Gyseghem, Elke Van; Jimidar, M.; Sneyers, R.; Redlich, Dirk; Verhoeven, E.; Massart, D.L.; Heyden, Yvan Vander

doi:10.1016/j.chroma.2005.03.085

Cited by 49 publications

(43 citation statements)

References 12 publications

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“…In such cases an alternative methodology might be used -the application of pairwise variable comparisons. Correlation or distance based matrices are most often used [24][25][26]. However, sometimes such matrices, especially in the form of heatmaps are used merely as a convenient representation of HCA results [24][25][26].…”

Section: Comparison Of Chromatographic Columns By Means Of Srd and Gpcmmentioning

confidence: 99%

How to compare separation selectivity of high-performance liquid chromatographic columns properly?

Andrić

Héberger

2017

Journal of Chromatography A

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Comparison and selection of chromatographic columns is an important part of development as well as validation of analytical methods. Presently there is abundant number of methods for selection of the most similar and orthogonal columns, based on the application of limited number of test compounds as well as quantitative structure retention relationship models (QSRR), from among Snyder's hydrophobicsubtraction model (HSM) have been most extensively used.Chromatographic data of 67 compounds were evaluated using principal component analysis (PCA), hierarchical cluster analysis (HCA), non-parametric ranking methods as sum of ranking differences (SRD) and generalized pairwise correlation method (GPCM), both applied as a consensus driven comparison, and complemented by the comparison with one variable at a time (COVAT) approach. The aim was to compare the ability of the HSM approach and the approach based on primary retention data of test solutes (logk values) to differentiate among ten highly similar C18 columns.The ranking (clustering) pattern of chromatographic columns based on primary retention data and HSM parameters gave different results in all instances. Patterns based on retention coefficients were in accordance with expectations based on columns' physicochemical parameters, while HSM parameters provided a different clustering.Similarity indices calculated from the following dissimilarity measures: SRD, GPCM Fisher's conditional exact probability weighted (CEPW) scores; Euclidian, Manhattan, Chebyshev, and cosine distances Pearson's, Spearman's, and Kendall's, correlation coefficients have been ranked by the consensus based SRD. Analysis of variance confirmed that the HSM model produced statistically significant increases of SRD values for the majority of similarity indices, i.e. HSM transformation of original retention data yields significant loss of information, and finally results in lower performance of HSM methodology. The best similarity measures were obtained using primary retention data, and derived from Kendal's and Spearman's correlation coefficients, as well as GPCM and SRD score values. Selectivity function, Fs, originally proposed by Snyder, demonstrated moderate performance.

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Section: Comparison Of Chromatographic Columns By Means Of Srd and Gpcmmentioning

confidence: 99%

How to compare separation selectivity of high-performance liquid chromatographic columns properly?

Andrić

Héberger

2017

Journal of Chromatography A

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“…Orthogonal column chromatography involves carrying out two independent separations of a mixture on columns with different chromatographic selectivity in order to increase the number of compounds that can be separated [1,2]. Using short columns and high temperatures, a reversed-phase separation can be carried out very quickly and with a high degree of retention time reproducibility on a quality LC system [37].…”

Section: Orthogonal Lcmentioning

confidence: 99%

“…Another option for increasing separation space in chromatography without special instrumentation is to carry out two independent separations on orthogonal columns [1,2]. Although such a technique is not a true two-dimensional separation, two orthogonal separations of 2.5 min each can potentially contain more information than a single, 5-min separation and can be done simply by injecting the sample onto two separate systems.…”

Section: Introductionmentioning

confidence: 99%

“…These methods include orthogonal chromatography, where a sample is separated independently using two different stationary phases [1,2], and comprehensive two-dimensional liquid chromatography (2D-LC), where sequential fractions of the eluent from a first column are introduced into a second column [3][4][5][6][7]. Although these methods provide increased peak capacity relative to conventional LC methods, there still is a high risk of peak overlap for complex samples, such as those encountered in proteomic and metabolomic investigations [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of the multivariate selectivity of multi‐way liquid chromatography methods

Cantwell

Porter

Rutan

2007

Journal of Chemometrics

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Multivariate figures of merit are used to characterize the sensitivity, selectivity, and precision of multi-dimensional liquid chromatography (LC) methods. Specifically in this work, we used a multivariate selectivity parameter to characterize the performance of orthogonal LC and twodimensional liquid chromatography (2D-LC) methods with and without diode array detection (DAD), and to aid in the optimization of these new separation approaches. We found that the average selectivity of 47 drug compounds was increased by as much as 58% upon the addition of a second, orthogonal separation. We also found that the selectivity for some individual drug compounds improved by more than a factor of two when DAD detection was used compared to single wavelength detection. In the comprehensive 2D-LC simulations, using a multi-channel detector increased the average selectivity of 500 components by 7.3% for a 21 s cycle time. The multivariate selectivity parameter was shown to be useful for determining the information gained when additional dimensions of data are collected on multi-dimensional instrumentation.

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“…In practice, there may also be correlation between theoretically orthogonal stationary phases, such as from secondary hydrophobic interactions in ion-exchange resins when coupled to an RP method. Adjusting the mobile phase and brand of stationary phase between two similar dimensions may reduce correlation, and orthogonality has been investigated by 'weightedaverage-linkage dendograms' (Van Gyseghema et al, 2005), which could be a useful starting point for choosing 'different' dimensions for a MD system with reduced correlation.…”

Section: Theoretical Aspects Of Multi-dimensional Liquid Chromatograpmentioning

confidence: 99%

Comprehensive multi‐dimensional liquid chromatographic separation in biomedical and pharmaceutical analysis: a review

Dixon

Pitfield

Perrett

2006

Biomedical Chromatography

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‘Multi‐dimensional’ liquid separations have a history almost as long as chromatography. In multi‐dimensional chromatography the sample is subjected to more than one separation mechanism; each mechanism is considered an independent separation dimension. The separations can be carried out either offline via fraction collection, or directly coupled online. Early multi‐dimensional separations using combinations of paper chromatography, electrophoresis and gels, in both planar and columnar modes are reviewed. Developments in HPLC have increased the number of measurable analytes in ever more complex matrices, and this has led to the concept of ‘global metabolite profiling’. This review focuses on the theory and practice of modern ‘comprehensive’ multi‐dimensional liquid chromatography when applied to biomedical and pharmaceutical analysis. Copyright © 2006 John Wiley & Sons, Ltd.

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Orthogonality and similarity within silica-based reversed-phased chromatographic systems

Cited by 49 publications

References 12 publications

How to compare separation selectivity of high-performance liquid chromatographic columns properly?

How to compare separation selectivity of high-performance liquid chromatographic columns properly?

Evaluation of the multivariate selectivity of multi‐way liquid chromatography methods

Comprehensive multi‐dimensional liquid chromatographic separation in biomedical and pharmaceutical analysis: a review

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