Abstract
Using current data analysis techniques, even the most advanced LC-MS instrumentation can identify only a small fraction of compounds found in typical biological extracts. Augmenting MS information with HPLC retention information allows many more to be identified. In fact, our calculations indicate that a quadrupole MS is able to identify more compounds than an FTICR-MS when the quadrupole spectrum is augmented with retention information. Unfortunately, retention information is extremely difficult to harness for compound identification. Here, we demonstrate the first use of isocratic data measured on one LC-MS to "project" gradient retention on to different LC-MS systems. Using 35 chemically diverse solutes chosen to encompass the full range of reversed-phase alkylsilica interactions, and using experimental conditions typical of metabolomics experiments, gradient retention was projected from one instrument to another with only 1.2-2.6% error-enough accuracy to considerably improve compound identification. Besides accounting for nonlinear relationships of retention versus solvent composition as well as dead time versus solvent composition, accounting for the precise shape of the gradient profile (not just the dwell volume) improved projection accuracy on one instrument by up to 4 fold whereas flow rate non-idealities likely caused considerable error on the other instrument. Thus, these two factors must be taken into account to accurately project retention on diverse instrumentation.
Original language | English (US) |
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Pages (from-to) | 6732-6741 |
Number of pages | 10 |
Journal | Journal of Chromatography A |
Volume | 1218 |
Issue number | 38 |
DOIs | |
State | Published - Sep 23 2011 |
Bibliographical note
Funding Information:We thank the National Science Foundation [ IOS-0923960 and MCB-0725149 ], the Minnesota Agricultural Experiment Station , and the Gordon and Margaret Bailey Endowment for Environmental Horticulture for financial support.
Keywords
- Chemical identification
- Cross-instrument
- Gradient profile
- Isocratic to gradient conversion
- Retention prediction
- Retention projection