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Journal of Chromatography & Separation Techniques | Volume: 09
8th World Congress on Chromatography
September 13-14, 2018 | Prague, Czech Republic
Polymer Science and Technology
4 th International Conference on
Joint Event on
&
Károly Héberger
RCNS- Hungarian Academy of
Sciences, Hungary
There are two legitimate aims for column selection: i) to determine similar
ones to an existing one and ii) to find diverse (orthogonal) one(s) for optimal
separation. Several different methods have already been elaborated to compare
selectivity of chromatographic columns. All comparisons realize empirical
approaches and based on measuring retention data of several well-chosen test
compounds. Proper multivariate analyses can find similarities and differences in
retention behavior of test compounds and stationary phases. As an illustration we
adopted Wilson et al.’s data of 67 test compounds and 10 highly similar columns
(C18-bonded silica stationary phases). The inherent characteristic groupings
by physical properties were revealed with correct statistical tests and several
independent methodologies. Generalized pair correlation method (GPCM)2 and
sum of (absolute) ranking differences (SRD)3,4 unambiguously showed the same
ranking pattern. The clustering by SRD is delivered to the reference. Therefore, all
columns have been chosen as gold standard once and only once (comparison with
one variable at a time). All lines of boxes correspond to an SRD ordering always
with a different reference column (Figure 1). COVAT heatmaps show destroying
the true pattern if the hydrophobic-subtraction model (HSM) evaluation is used.
The ranking (clustering) pattern of chromatographic columns based on retention
data (log k values) of 67 compounds and selectivity parameters of hydrophobic-
subtraction model (HSM) provided various
column groupings. Loss of information is
inevitable for using the HSM data handling.
Processing of retention data resulted in
Karoly Heberger, J Chromatogr Sep Tech 2018, Volume: 09
DOI: 10.4172/2157-7064-C2-042
Separation selectivityof liquidchromatographic columns: a comparisonbynonparametricmethods
patterns that are consistent with differences in the columns’ physicochemical parameters,
whereas HSM results are deviating to a higher or lesser degree, depending on the particular
chemometric approach. GPCM, SRD and COVAT procedures can be carried out on any data
sets partially and on the whole to select the most similar and dissimilar columns, though our
calculations were completed to the data set of Wilson et al.
Biography
Károly Héberger has completed his PhD, Cand. scient., DSc and t. Prof. In his early career, he investigated liquid phase oxidation (radical) processes and
determined rate constants by kinetic ESR spectroscopy. Later, he studied quantitative structure activity (property) relationships like QSAR, QSPR and QSRR.
Now, he deals with chemometrics such as multivariate data evaluation techniques, principal component analysis, stepwise linear regression, partial least squares
regression, variable selection, model building and validation, pattern recognition (supervised and unsupervised), classification of food products, clustering, method
comparison and ranking etc. His scientific results were presented in more than 160 papers (including book chapters) and has given more than 300 lectures (or
posters) with h-index=34 and i-10 index=83 (Web of Science). The papers were cited above 3500 times.
Figure 1. Heatmap plot of SRD analysis using primary retention data (67 test compounds)
and comparison of one variable at a time.