Harmonized quality assurance/quality control provisions to assess completeness and robustness of MS1 data preprocessing for LC-HRMS-based suspect screening and non-targeted analysis

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Authors

LENNON Sarah CHAKER Jade PRICE Elliott James HOLLENDER Juliane HUBER Carolin SCHULZE Tobias AHRENS Lutz BEEN Frederic CREUSOT Nicolas DEBRAUWER Laurent DERVILLY Gaud GABRIEL Catherine GUERIN Thierry HABCHI Baninia JAMIN Emilien L. KLÁNOVÁ Jana KOSJEK Tina LE BIZEC Bruno MEIJER Jeroen MOL Hans NIJSSEN Rosalie OBERACHER Herbert PAPAIOANNOU Nafsika PARINET Julien SARIGIANNIS Dimosthenis STRAVS Michael A. TKALEC Žiga SCHYMANSKI Emma L. LAMOREE Marja ANTIGNAC Jean-Philippe DAVID Arthur

Year of publication 2024
Type Article in Periodical
Magazine / Source TrAC Trends in Analytical Chemistry
MU Faculty or unit

Faculty of Science

Citation
Web https://www.sciencedirect.com/science/article/pii/S0165993624001560?via%3Dihub
Doi http://dx.doi.org/10.1016/j.trac.2024.117674
Keywords High-resolution mass spectrometry; Exposomics; Metabolomics; Non-targeted analysis; Suspect screening analysis; Data preprocessing; Contaminants of emerging concern; Chemical exposome; Harmonized QA/QC
Description Non-targeted and suspect screening analysis using liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) holds great promise to comprehensively characterize complex chemical mixtures. Data preprocessing is a crucial part of the process, however, some limitations are observed: (i) peak-picking and feature extraction might be incomplete, especially for low abundant compounds, and (ii) limited reproducibility has been observed between laboratories and software for detected features and their relative quantification. We first conducted a critical review of existing solutions that could improve the reproducibility of preprocessing for LC-HRMS. Solutions include providing repositories and reporting guidelines, open and modular processing workflows, public benchmark datasets, tools to optimize the data preprocessing and to filter out false positive detections. We then propose harmonized quality assurance/quality control guidelines that would allow to assess the sensitivity of feature detection, reproducibility, integration accuracy, precision, accuracy, and consistency of data preprocessing for human biomonitoring, food and environmental communities.
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