"Retention projection" enables reliable use of shared gas chromatographic retention data across laboratories, instruments, and methods

“保留投影”可实现跨实验室、仪器和方法的共享气相色谱保留数据的可靠使用

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作者:Brian B Barnes, Michael B Wilson, Peter W Carr, Mark F Vitha, Corey D Broeckling, Adam L Heuberger, Jessica Prenni, Gregory C Janis, Henry Corcoran, Nicholas H Snow, Shilpi Chopra, Ramkumar Dhandapani, Amanda Tawfall, Lloyd W Sumner, Paul G Boswell

Abstract

Gas chromatography/mass spectrometry (GC/MS) is a primary tool used to identify compounds in complex samples. Both mass spectra and GC retention times are matched to those of standards; however, it is often impractical to have standards on hand for every compound of interest, so we must rely on shared databases of MS data and GC retention information. Unfortunately, retention databases (e.g., linear retention index libraries) are experimentally restrictive, notoriously unreliable, and strongly instrument dependent, relegating GC retention information to a minor, often negligible role in compound identification despite its potential power. A new methodology called "retention projection" has great potential to overcome the limitations of shared chromatographic databases. In this work, we tested the reliability of the methodology in five independent laboratories. We found that, even when each lab ran nominally the same method, the methodology was 3-fold more accurate than retention indexing because it properly accounted for unintentional differences between the GC/MS systems. When the laboratories used different methods of their own choosing, retention projections were 4- to 165-fold more accurate. More importantly, the distribution of error in the retention projections was predictable across different methods and laboratories, thus enabling automatic calculation of retention time tolerance windows. Tolerance windows at 99% confidence were generally narrower than those widely used even when physical standards are on hand to measure their retention. With its high accuracy and reliability, the new retention projection methodology makes GC retention a reliable, precise tool for compound identification, even when standards are not available to the user.

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