Retention Time Trajectory Matching for Peak Identification in Chromatographic Analysis

Author:

Zang Wenzhe123,Sharma Ruchi123,Li Maxwell Wei-Hao1234,Fan Xudong123ORCID

Affiliation:

1. Department of Biomedical Engineering, University of Michigan, 1101 Beal Avenue, Ann Arbor, MI 48109, USA

2. Center for Wireless Integrated MicroSensing and Systems (WIMS2), University of Michigan, Ann Arbor, MI 48109, USA

3. Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor, MI 48109, USA

4. Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA

Abstract

Retention time drift caused by fluctuations in physical factors such as temperature ramping rate and carrier gas flow rate is ubiquitous in chromatographic measurements. Proper peak matching and identification across different chromatograms is critical prior to any subsequent analysis but is challenging without using mass spectrometry. The purpose of this work was to describe and validate a peak matching and identification method called retention time trajectory (RTT) matching that can be used in targeted analyses free of mass spectrometry. This method uses chromatographic retention times as the only input and identifies peaks associated with any subset of a predefined set of target compounds. An RTT is a two-dimensional (2D) curve formed uniquely by the retention times of the chromatographic peaks. The RTTs obtained from the chromatogram of a sample under test and those pre-installed in a library are matched and statistically compared. The best matched pair implies identification. Unlike most existing peak-alignment methods, no mathematical warping or transformation is involved. Based on the experimentally characterized RTT, an RTT hybridization method was also developed to rapidly generate more RTTs and expand the library without performing actual time-consuming chromatographic measurements, which enables successful peak matching even for chromatograms with severe retention time drifts. Additionally, 3.15 × 105 tests using experimentally obtained gas chromatograms and 2 × 1012 tests using two publicly available fruit metabolomics datasets validated the proposed method, demonstrating real-time peak/interferent identification.

Funder

National Institute for Occupational Safety and Health

Office of the Director of National Intelligence

Intelligence Advanced Research Projects Activity

University of Michigan Richard A Auhll Professorship

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

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