van Herwerden Denice, O'Brien Jake W, Lege Sascha, Pirok Bob W J, Thomas Kevin V, Samanipour Saer
Van 't Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Amsterdam 1012 WX, The Netherlands.
Queensland Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Brisbane 4102, Australia.
Anal Chem. 2023 Aug 22;95(33):12247-12255. doi: 10.1021/acs.analchem.3c00896. Epub 2023 Aug 7.
Clean high-resolution mass spectra (HRMS) are essential to a successful structural elucidation of an unknown feature during nontarget analysis (NTA) workflows. This is a crucial step, particularly for the spectra generated during data-independent acquisition or during direct infusion experiments. The most commonly available tools only take advantage of the time domain for spectral cleanup. Here, we present an algorithm that combines the time domain and mass domain information to perform spectral deconvolution. The algorithm employs a probability-based cumulative neutral loss (CNL) model for fragment deconvolution. The optimized model, with a mass tolerance of 0.005 Da and a score threshold of 0.00, was able to achieve a true positive rate (TPr) of 95.0%, a false discovery rate (FDr) of 20.6%, and a reduction rate of 35.4%. Additionally, the CNL model was extensively tested on real samples containing predominantly pesticides at different concentration levels and with matrix effects. Overall, the model was able to obtain a TPr above 88.8% with FD rates between 33 and 79% and reduction rates between 9 and 45%. Finally, the CNL model was compared with the retention time difference method and peak shape correlation analysis, showing that a combination of correlation analysis and the CNL model was the most effective for fragment deconvolution, obtaining a TPr of 84.7%, an FDr of 54.4%, and a reduction rate of 51.0%.
在非目标分析（NTA）工作流程中，清晰的高分辨率质谱（HRMS）对于成功解析未知特征的结构至关重要。这是关键的一步，特别是对于在数据非依赖型采集或直接进样实验过程中生成的质谱图。最常用的工具仅利用时域进行谱图清理。在此，我们提出一种结合时域和质量域信息来进行谱图去卷积的算法。该算法采用基于概率的累积中性损失（CNL）模型进行碎片去卷积。优化后的模型，质量容差为0.005 Da，得分阈值为0.00，能够实现95.0%的真阳性率（TPr）、20.6%的错误发现率（FDr）以及35.4%的降低率。此外，CNL模型在主要含有不同浓度水平农药且存在基质效应的实际样品上进行了广泛测试。总体而言，该模型能够获得高于88.8%的TPr，FDr在33%至79%之间，降低率在9%至45%之间。最后，将CNL模型与保留时间差异法和峰形相关性分析进行比较，结果表明相关性分析与CNL模型相结合对于碎片去卷积最为有效，获得了84.7%的TPr、54.4%的FDr以及51.0%的降低率。
