TY - JOUR
T1 - 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
AU - Lennon, Sarah
AU - Chaker, Jade
AU - Price, Elliott J.
AU - Hollender, Juliane
AU - Huber, Carolin
AU - Schulze, Tobias
AU - Ahrens, Lutz
AU - Béen, Frederic
AU - Creusot, Nicolas
AU - Debrauwer, Laurent
AU - Dervilly, Gaud
AU - Gabriel, Catherine
AU - Guérin, Thierry
AU - Habchi, Baninia
AU - Jamin, Emilien L.
AU - Klánová, Jana
AU - Kosjek, Tina
AU - Le Bizec, Bruno
AU - Meijer, Jeroen
AU - Mol, Hans
AU - Nijssen, Rosalie
AU - Oberacher, Herbert
AU - Papaioannou, Nafsika
AU - Parinet, Julien
AU - Sarigiannis, Dimosthenis
AU - Stravs, Michael A.
AU - Tkalec, Žiga
AU - Schymanski, Emma L.
AU - Lamoree, Marja
AU - Antignac, Jean Philippe
AU - David, Arthur
PY - 2024/5
Y1 - 2024/5
N2 - 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.
AB - 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.
KW - Chemical exposome
KW - Contaminants of emerging concern
KW - Data preprocessing
KW - Exposomics
KW - Harmonized QA/QC
KW - High-resolution mass spectrometry
KW - Metabolomics
KW - Non-targeted analysis
KW - Suspect screening analysis
U2 - 10.1016/j.trac.2024.117674
DO - 10.1016/j.trac.2024.117674
M3 - Article
AN - SCOPUS:85189750851
SN - 0165-9936
VL - 174
JO - TrAC - Trends in Analytical Chemistry
JF - TrAC - Trends in Analytical Chemistry
M1 - 117674
ER -