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Retention Time Trajectory Matching for Peak Identification in Chromatographic Analysis



Details

  • Personal Author:
  • Description:
    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. [Description provided by NIOSH]
  • Subjects:
  • Keywords:
  • ISSN:
    0746-9462
  • Document Type:
    Text
  • Funding:
    Grant
  • Genre:
    Journal Article
  • Place as Subject:
  • CIO:
    National Institute for Occupational Safety and Health (NIOSH)
  • Topic:
    Workplace Safety & Health
  • Location:
    North America
  • Volume:
    23
  • Issue:
    13
  • NIOSHTIC Number:
    nn:20068011
  • Citation:
    Sensors 2023 Jul; 23(13):6029
  • Contact Point Address:
    Xudong Fan, Department of Biomedical Engineering, University of Michigan, 1101 Beal Avenue, Ann Arbor, MI 48109, USA
  • Email:
    xsfan@umich.edu
  • Federal Fiscal Year:
    2023
  • Performing Organization:
    University of Michigan at Ann Arbor
  • Peer Reviewed:
    True
  • Start Date:
    20180901
  • Source Full Name:
    Sensors
  • End Date:
    20220831
  • Collection(s):
  • Main Document Checksum:
    urn:sha-512:ad7ce0cc07b94fea708e3d81ce8f354601b861d8d369a7edf6c6d5b36ba37ea20c06063e92f50869288daaadb6f5e0872e73d4806c12b5ff6006ec55e16a76cb
  • Download URL:
  • File Type:
    Filetype[PDF - 2.33 MB ]
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