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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| Published in: | Analytical and bioanalytical chemistry Vol. 417; no. 27; pp. 6065 - 6073 |
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| Main Authors: | , , , , |
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01.11.2025
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| Abstract | 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. |
|---|---|
| AbstractList | 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 (δm/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.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 (δm/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. 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 ) 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. 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 (δm/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. 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. 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. |
| Author | Tomasi, Giorgio Christensen, Jan H. Kronik, Oskar Munk Nielsen, Nikoline Juul Tisler, Selina |
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| Cites_doi | 10.1016/j.chemolab.2021.104333 10.1016/j.chroma.2015.11.014 10.1016/j.mex.2023.102199 10.1016/j.chroma.2022.463501 10.1038/protex.2015.102 10.1021/acs.analchem.4c00494 10.1021/acs.est.7b02184 10.1016/j.chroma.2017.04.052 10.1016/j.trac.2016.07.004 10.1016/j.aca.2018.04.003 10.1021/acs.analchem.2c04538 10.1016/j.watres.2022.118599 10.1186/1471-2105-9-504 10.1093/jxb/eri068 10.1186/s12859-019-2848-8 10.1021/ac050980b 10.1021/acs.analchem.7b01069 10.1021/es5002105 10.1021/acs.analchem.3c01079 10.1007/s00216-016-9700-z 10.1016/S0169-7439(00)00071-X 10.1007/978-3-319-93764-9_9 |
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| Keywords | Objective parameterisation High-resolution mass spectrometry Non-target screening Region of interest Data preprocessing Chromatography |
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| References_xml | – ident: 5718_CR12 doi: 10.1016/j.chemolab.2021.104333 – volume: 1426 start-page: 77 year: 2015 ident: 5718_CR17 publication-title: J Chromatogr A doi: 10.1016/j.chroma.2015.11.014 – ident: 5718_CR10 doi: 10.1016/j.mex.2023.102199 – volume: 1682 start-page: 463501 year: 2022 ident: 5718_CR16 publication-title: J Chromatogr A doi: 10.1016/j.chroma.2022.463501 – ident: 5718_CR8 doi: 10.1038/protex.2015.102 – volume: 96 start-page: 7120 year: 2024 ident: 5718_CR19 publication-title: Anal Chem doi: 10.1021/acs.analchem.4c00494 – volume: 51 start-page: 11505 year: 2017 ident: 5718_CR1 publication-title: Environ Sci Technol doi: 10.1021/acs.est.7b02184 – start-page: 85 volume-title: Mass spectrometry - principles and applications year: 2007 ident: 5718_CR21 – volume: 1503 start-page: 57 year: 2017 ident: 5718_CR11 publication-title: J Chromatogr A doi: 10.1016/j.chroma.2017.04.052 – volume: 82 start-page: 425 year: 2016 ident: 5718_CR3 publication-title: TrAC Trends Anal Chem doi: 10.1016/j.trac.2016.07.004 – volume: 1025 start-page: 80 year: 2018 ident: 5718_CR9 publication-title: Anal Chim Acta doi: 10.1016/j.aca.2018.04.003 – volume: 95 start-page: 1513 year: 2023 ident: 5718_CR23 publication-title: Anal Chem doi: 10.1021/acs.analchem.2c04538 – ident: 5718_CR15 doi: 10.1016/j.watres.2022.118599 – volume: 9 start-page: 1 year: 2008 ident: 5718_CR6 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-9-504 – volume: 56 start-page: 273 year: 2005 ident: 5718_CR4 publication-title: J Exp Bot doi: 10.1093/jxb/eri068 – volume: 20 start-page: 1 year: 2019 ident: 5718_CR22 publication-title: BMC Bioinformatics doi: 10.1186/s12859-019-2848-8 – volume: 78 start-page: 975 year: 2006 ident: 5718_CR7 publication-title: Anal Chem doi: 10.1021/ac050980b – volume: 89 start-page: 8689 year: 2017 ident: 5718_CR18 publication-title: Anal Chem doi: 10.1021/acs.analchem.7b01069 – volume: 48 start-page: 2097 year: 2014 ident: 5718_CR2 publication-title: Environ Sci Technol doi: 10.1021/es5002105 – volume: 95 start-page: 13804 year: 2023 ident: 5718_CR20 publication-title: Anal Chem doi: 10.1021/acs.analchem.3c01079 – volume: 408 start-page: 5855 year: 2016 ident: 5718_CR5 publication-title: Anal Bioanal Chem doi: 10.1007/s00216-016-9700-z – volume: 52 start-page: 1 year: 2000 ident: 5718_CR13 publication-title: Chemom Intell Lab Syst doi: 10.1016/S0169-7439(00)00071-X – ident: 5718_CR14 doi: 10.1007/978-3-319-93764-9_9 |
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| Snippet | Liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) is commonly used for identification of compounds in complex samples due to the... |
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| SubjectTerms | 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 |
| Subtitle | A fast region of interest algorithm for efficient data compression and improved peak detection in high-resolution mass spectrometry |
| Title | A fast region of interest algorithm for efficient data compression and improved peak detection in high-resolution mass spectrometry |
| URI | https://link.springer.com/article/10.1007/s00216-024-05718-7 https://www.ncbi.nlm.nih.gov/pubmed/39786495 https://www.proquest.com/docview/3268266569 https://www.proquest.com/docview/3153915307 |
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