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

Overview of attention for article published in Journal of Chromatography A, March 2017
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Title
How to compare separation selectivity of high-performance liquid chromatographic columns properly?
Published in
Journal of Chromatography A, March 2017
DOI 10.1016/j.chroma.2017.01.066
Pubmed ID
Authors

Filip Andrić, Károly Héberger

Abstract

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 hydrophobic-subtraction 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. HS 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.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 1 4%
Unknown 25 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 23%
Researcher 5 19%
Student > Bachelor 5 19%
Professor 2 8%
Other 1 4%
Other 4 15%
Unknown 3 12%
Readers by discipline Count As %
Chemistry 13 50%
Chemical Engineering 2 8%
Environmental Science 2 8%
Pharmacology, Toxicology and Pharmaceutical Science 1 4%
Unknown 8 31%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 March 2017.
All research outputs
#11,859,419
of 19,459,575 outputs
Outputs from Journal of Chromatography A
#8,726
of 11,826 outputs
Outputs of similar age
#146,254
of 272,738 outputs
Outputs of similar age from Journal of Chromatography A
#27
of 103 outputs
Altmetric has tracked 19,459,575 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,826 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 26th percentile – i.e., 26% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 272,738 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 103 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.