Liquid chromatography combined with high-resolution mass spectrometry (LC-HRMS) is a frequently applied technique for suspect screening (SS) and non-target screening (NTS) in metabolomics and environmental toxicology. However, correctly identifying compounds based on SS or NTS approaches remains challenging, especially when using data-independent acquisition (DIA). This study assessed the performance of four HRMS-spectra identification tools to annotate in-house generated data-dependent acquisition (DDA) and DIA HRMS spectra of 32 pesticides, veterinary drugs, and their metabolites. The identification tools were challenged with a diversity of compounds, including isomeric compounds. The identification power was evaluated in solvent standards and spiked feed extract. In DDA spectra, the mass spectral library mzCloud provided the highest success rate, with 84% and 88% of the compounds correctly identified in the top three in solvent standard and spiked feed extract, respectively. The in silico tools MSfinder, CFM-ID, and Chemdistiller also performed well in DDA data, with identification success rates above 75% for both solvent standard and spiked feed extract. MSfinder provided the highest identification success rates using DIA spectra with 72% and 75% (solvent standard and spiked feed extract, respectively), and CFM-ID performed almost similarly in solvent standard and slightly less in spiked feed extract (72% and 63%). The identification success rates for Chemdistiller (66% and 38%) and mzCloud (66% and 31%) were lower, especially in spiked feed extract. The difference in success rates between DDA and DIA is most likely caused by the higher complexity of the DIA spectra, making direct spectral matching more complex. However, this study demonstrates that DIA spectra can be used for compound annotation in certain software tools, although the success rate is lower than for DDA spectra.

中文翻译：

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使用数据依赖型和数据独立型高分辨率质谱图进行化合物鉴定工具的比较

液相色谱结合高分辨率质谱（LC-HRMS）是代谢组学和环境毒理学中可疑筛查（SS）和非目标筛查（NTS）的常用技术。然而，基于 SS 或 NTS 方法正确识别化合物仍然具有挑战性，特别是在使用数据独立采集 (DIA) 时。本研究评估了四种 HRMS 光谱识别工具的性能，以注释内部生成的 32 种农药、兽药及其代谢物的数据依赖采集 (DDA) 和 DIA HRMS 光谱。鉴定工具面临着多种化合物的挑战，包括异构体化合物。在溶剂标准品和加标饲料提取物中评估识别能力。在 DDA 谱图中，质谱库 mzCloud 提供了最高的成功率，在溶剂标准品和加标饲料提取物中分别正确识别出前三名的化合物，分别为 84% 和 88%。计算机工具 MSfinder、CFM-ID 和 Chemdistiller 在 DDA 数据中也表现良好，溶剂标准品和加标饲料提取物的识别成功率均超过 75%。MSfinder 使用 DIA 谱图提供了最高的识别成功率，分别为 72% 和 75%（分别为溶剂标准品和加标饲料提取物），而 CFM-ID 在溶剂标准品中的表现几乎相似，但在加标饲料提取物中的表现稍差（72% 和 63%） ）。Chemdistiller（66% 和 38%）和 mzCloud（66% 和 31%）的识别成功率较低，特别是在加标饲料提取物中。DDA 和 DIA 之间成功率的差异很可能是由于 DIA 光谱的复杂性较高，使得直接光谱匹配更加复杂。然而，本研究表明，DIA 谱图可用于某些软件工具中的化合物注释，尽管成功率低于 DDA 谱图。