Improving peak capacities over 100 in less than 60 seconds: operating above normal peak capacity limits with signal processing.

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ID: 89328
2020
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Abstract
A primary focus in liquid chromatography analysis of complex samples is high peak capacity separations. Using advanced instrumentation and optimal small, high-efficiency columns, complex multicomponent mixtures can now be analyzed in relatively short times. Despite these advances, chromatographic peak overlap is still observed. Recently, attention has shifted from improvements in chromatographic efficiency and selectivity to enhancing data processing after collection. Curve fitting methods can be used to trace underlying peaks, but do not directly enhance chromatographic resolution. Methods based on the properties of derivatives and power transform were recently shown to enhance chromatographic peak resolution while maintaining critical peak information (peak areas and retention times). These protocols have been extensively investigated for their fundamental properties, advantages, and limitations, but they have not been evaluated with complex chromatograms. Herein, we evaluate the use of deconvolution via Fourier transform (FT), even-derivative peak sharpening, and power law with the fast separation (< 60 s) of a 101-component mixture using ultra-high-pressure liquid chromatography. High noise and peak overlap are present in this gradient separation, which is representative of fast chromatography. Chromatographic resolution enhancement is demonstrated and described. Further, accurate quantitation is maintained and shown with representative examples. Enhancements in peak capacity and peak-to-peak resolutions are discussed. Finally, the statistical theory of overlap is used for 101 peaks and predictions are made for the number of singlet, doublet, and multiplets analyte peaks. The effect of increasing peak capacity by FT even derivative sharpening and power laws leads to a decrease in the number of peak overlaps and an increase in total peak number. Graphical abstract.
Reference Key
hellinghausen2020improvinganalytical Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Hellinghausen, Garrett;Wahab, M Farooq;Armstrong, Daniel W;
Journal Analytical and bioanalytical chemistry
Year 2020
DOI
10.1007/s00216-020-02444-8
URL
Keywords
resolution fourier transform derivatives power law signal-to-noise ratio ultra-fast separation

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