预处理器
质量保证
计算机科学
数据挖掘
数据预处理
数据质量
稳健性(进化)
再现性
人工智能
工程类
化学
色谱法
生物化学
基因
运营管理
公制(单位)
外部质量评估
作者
Sarah Lennon,Jade Chaker,Elliott J. Price,Juliane Hollender,Carolin Huber,Tobias Schulze,Lutz Ahrens,Frederic Béen,Nicolas Creusot,Laurent Debrauwer,Gaud Dervilly,Catherine Gabriel,Thierry Guérin,Baninia Habchi,Emilien L. Jamin,Jana Klánová,Tina Kosjek,Bruno Le Bizec,Jeroen Meijer,Hans Mol
标识
DOI:10.1016/j.trac.2024.117674
摘要
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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