Literature DB >> 21840007

Easy and accurate high-performance liquid chromatography retention prediction with different gradients, flow rates, and instruments by back-calculation of gradient and flow rate profiles.

Paul G Boswell1, Jonathan R Schellenberg, Peter W Carr, Jerry D Cohen, Adrian D Hegeman.   

Abstract

Isocratic retention data should make a suitable foundation for an accurate, cross-instrument LC retention prediction system. Our previous work suggested that in order to accurately calculate (or "project") gradient retention times on a wide range of HPLC systems using a single set of isocratic retention data, the precise shape of both the gradient and flow rate profiles produced by each instrument must be properly taken into account. However, accurate measurement of these system properties is difficult and time-consuming. In this work, we describe an approach that uses the measured gradient retention times of a set of standard solutes spiked into the sample along with their known isocratic retention vs. eluent composition relationships to determine the effective gradient and flow rate profiles by back-calculation. Retention "projections" of 20 other solutes using these back-calculated profiles, under various chromatographic conditions typical of metabolomics experiments, were remarkably accurate (as good as 0.23% of the gradient time, R2 up to 0.99996), being very near the level of retention reproducibility. Our calculations suggest that this level of accuracy will allow a quadrupole MS to identify 38-fold more compounds out of a simulated mixture of 7307; it would allow an FTICR-MS to improve its identification rate nearly two-fold with the same mixture. Moreover, very little effort is required of the user. This approach provides a simple way to correct for all instrument-related factors affecting retention, allowing dramatically streamlined and improved retention projection across gradients, flow rates, and HPLC instruments.
Copyright © 2011 Elsevier B.V. All rights reserved.

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Year:  2011        PMID: 21840007     DOI: 10.1016/j.chroma.2011.07.070

Source DB:  PubMed          Journal:  J Chromatogr A        ISSN: 0021-9673            Impact factor:   4.759


  8 in total

1.  Retention projection enables accurate calculation of liquid chromatographic retention times across labs and methods.

Authors:  Daniel Abate-Pella; Dana M Freund; Yan Ma; Yamil Simón-Manso; Juliane Hollender; Corey D Broeckling; David V Huhman; Oleg V Krokhin; Dwight R Stoll; Adrian D Hegeman; Tobias Kind; Oliver Fiehn; Emma L Schymanski; Jessica E Prenni; Lloyd W Sumner; Paul G Boswell
Journal:  J Chromatogr A       Date:  2015-08-03       Impact factor: 4.759

2.  Metabolome Wide Association Study of serum DDT and DDE in Pregnancy and Early Postpartum.

Authors:  Xin Hu; Shuzhao Li; Piera Cirillo; Nickilou Krigbaum; ViLinh Tran; Tomoko Ishikawa; Michele A La Merrill; Dean P Jones; Barbara Cohn
Journal:  Reprod Toxicol       Date:  2019-05-15       Impact factor: 3.143

3.  Statistical analysis of isocratic chromatographic data using Bayesian modeling.

Authors:  Agnieszka Kamedulska; Łukasz Kubik; Paweł Wiczling
Journal:  Anal Bioanal Chem       Date:  2022-03-28       Impact factor: 4.478

4.  Accurate prediction of retention in hydrophilic interaction chromatography by back calculation of high pressure liquid chromatography gradient profiles.

Authors:  Nu Wang; Paul G Boswell
Journal:  J Chromatogr A       Date:  2017-08-26       Impact factor: 4.759

Review 5.  Computational Metabolomics: A Framework for the Million Metabolome.

Authors:  Karan Uppal; Douglas I Walker; Ken Liu; Shuzhao Li; Young-Mi Go; Dean P Jones
Journal:  Chem Res Toxicol       Date:  2016-10-12       Impact factor: 3.739

6.  Easy and accurate calculation of programmed temperature gas chromatographic retention times by back-calculation of temperature and hold-up time profiles.

Authors:  Paul G Boswell; Peter W Carr; Jerry D Cohen; Adrian D Hegeman
Journal:  J Chromatogr A       Date:  2012-09-23       Impact factor: 4.759

7.  MetaDB a Data Processing Workflow in Untargeted MS-Based Metabolomics Experiments.

Authors:  Pietro Franceschi; Roman Mylonas; Nir Shahaf; Matthias Scholz; Panagiotis Arapitsas; Domenico Masuero; Georg Weingart; Silvia Carlin; Urska Vrhovsek; Fulvio Mattivi; Ron Wehrens
Journal:  Front Bioeng Biotechnol       Date:  2014-12-16

8.  The METLIN small molecule dataset for machine learning-based retention time prediction.

Authors:  Xavier Domingo-Almenara; Carlos Guijas; Elizabeth Billings; J Rafael Montenegro-Burke; Winnie Uritboonthai; Aries E Aisporna; Emily Chen; H Paul Benton; Gary Siuzdak
Journal:  Nat Commun       Date:  2019-12-20       Impact factor: 14.919

  8 in total

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