A fast region of interest algorithm for efficient data compression and improved peak detection in high-resolution mass spectrometry A fast region of interest algorithm for efficient data compression and improved peak detection in high-resolution mass spectrometry

Liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) is commonly used for identification of compounds in complex samples due to the high chromatographic and mass spectral resolution provided. In subsequent data processing workflows, it is imperative to preserve this resolutio...

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Vydáno v:Analytical and bioanalytical chemistry Ročník 417; číslo 27; s. 6065 - 6073
Hlavní autoři: Kronik, Oskar Munk, Christensen, Jan H., Nielsen, Nikoline Juul, Tisler, Selina, Tomasi, Giorgio
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2025
Springer Nature B.V
Témata:
Algorithms
Analytical Chemistry
Biochemistry
Characterization and Evaluation of Materials
Chemistry
Chemistry and Materials Science
Chromatography
Compression
Computational Mass Spectrometry for Exposomics in Non-Target Screening
Data compression
Data processing
Datasets
Food Science
High resolution
Laboratory Medicine
Liquid chromatography
Mass spectrometry
Mass spectroscopy
Monitoring/Environmental Analysis
Noise threshold
Research Paper
Scientific imaging
Software
Spectral resolution
Objective parameterisation
High-resolution mass spectrometry
Non-target screening
Region of interest
Data preprocessing
Chromatography
ISSN:1618-2642, 1618-2650, 1618-2650
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Shrnutí:Liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) is commonly used for identification of compounds in complex samples due to the high chromatographic and mass spectral resolution provided. In subsequent data processing workflows, it is imperative to preserve this resolution to fully exploit the data. “Region of interest” (ROI) algorithms were introduced as a better alternative to equidistant binning for compressing HRMS data because they better preserve the mass spectral resolution. In this paper, we present a new ROI algorithm that improves on the selection of contiguous m / z traces, amongst others by introducing the concept of chromatographic filter, allows for an automated approach to optimise the admissible mass-to-charge deviation ( δ m/z ) and can be used to match ROIs across multiple samples. The algorithm was tested on a LC-HRMS dataset comprised of 21 replicate injections of a wastewater effluent extract and assessed on its ability to correctly retrieve the ROI’s relative to 57 compounds and match them across all injections. In summary, it achieved a ten-fold compression rate in on-disk storage at a noise threshold of 200 counts, and the median ROI length matched the observed chromatographic peak width (12–23 points). Correct ROI matching with a mass accuracy of 9 ppm was observed for 52 compounds across all 21 injections with only one compound split between two adjacent m/z traces in six runs. Overall, the new algorithm performed favourably compared to the ROI algorithm currently used in the well-established ROI-MCR (multivariate curve resolution) workflow for deconvolution of HRMS chromatographic data.
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ISSN:1618-2642
1618-2650
1618-2650
DOI:10.1007/s00216-024-05718-7