Data processing workflows for non-target screening on LC×LC-HRMS data (Oskar Munk Kronik, MDCW 2025)

🎤 Presenter:  Oskar Munk Kronik (niversity of Copenhagen, Frederiksberg, Denmark)

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💡 Book in your calendar: 17th Multidimensional Chromatography Workshop (MDCW) 13 - 15. January 2026

Abstract

Comprehensive two-dimensional liquid chromatography (LC×LC) has over the years matured from home-build intricate systems to commercially available solutions allowing for its proliferation in a broader community. A large body of research on LC×LC has focused on reaching optimal chromatographic conditions through various optimization schemes. Whereas the chromatographic performance of LC×LC outcompetes one-dimensional LC, the subsequent data processing is often laborious. Therefore, the data from LC×LC hyphenated to high-resolution mass spectrometry (HRMS) is often vastly underexploited. Even routine data processing operations for LC-HRMS remain challenging in LC×LC-HRMS.

In this study, we explore open-source data processing workflows for LC×LC-HRMS and assess their potential for non-target screening. Key aspects include:

1) data compression while preserving mass spectral resolution,
2) leveraging high-dimensional data through curve-resolution methods,
3) feasibility for trace-level analysis,
4) compound grouping across different samples, and
5) using LC×LC-HRMS to detect chemical differences between samples.

We benchmark these workflows using LC×LC-HRMS data from environmental and plant metabolomics samples.

In recent work (unpublished), we have investigated how to extract high-quality mass spectra for trace-level compounds in LC×LC-HRMS data using variable selection prior to multivariate curve-resolution (MCR). We found that variable selection is a key step for increasing the detection-rate of trace-level compounds when using MCR.