Orthogonal separations: Comparison of orthogonality metrics by statistical analysis

Journal of Chromatography. a
Mark R Schure, Joe M Davis

Abstract

Twenty orthogonality metrics (OMs) derived from convex hull, information theory, fractal dimension, correlation coefficients, nearest neighbor distances and bin-density techniques were calculated from a diverse group of 47 experimental two-dimensional (2D) chromatograms. These chromatograms comprise two datasets; one dataset is a collection of 2D chromatograms from Peter Carr's laboratory at the University of Minnesota, and the other dataset is based on pairs of one-dimensional chromatograms previously published by Martin Gilar and coworkers (Waters Corp.). The chromatograms were pooled to make a third or combined dataset. Cross-correlation results suggest that specific OMs are correlated within families of nearest neighbor methods, correlation coefficients and the information theory methods. Principal component analysis of the OMs show that none of the OMs stands out as clearly better at explaining the data variance than any another OM. Principal component analysis of individual chromatograms shows that different OMs favor certain chromatograms. The chromatograms exhibit a range of quality, as subjectively graded by nine experts experienced in 2D chromatography. The subjective (grading) evaluations were taken at two intervals ...Continue Reading

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Nov 18, 2011·Journal of Chromatography. a·Mark R Schure, Joe M Davis
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Citations

Feb 26, 2020·Journal of Separation Science·Tijmen S BosBob W J Pirok
Oct 14, 2017·Journal of Separation Science·Bob W J PirokPeter J Schoenmakers
Feb 1, 2021·Analytica Chimica Acta·Thomas ThemelisSebastiaan Eeltink
Feb 25, 2019·Journal of Chromatography. a·Rebecca K LindseyJ Ilja Siepmann
Aug 22, 2020·Journal of Chromatography. a·Camille L BilodeauSteven M Cramer
Dec 10, 2016·Analytical Chemistry·Dwight R Stoll, Peter W Carr
Oct 30, 2021·Biotechnology and Bioengineering·Nicholas VecchiarelloSteven Cramer

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