Abstract Chemical profiling with gas chromatography/mass spectrometry (GC/MS) full-spectrum acquisition mode often leads to the discovery of known-unknown components. These are non-identified components which arise from the limitation of the data processing method or limited breadth of mass spectral libraries. The recent introduction of the NIST Hybrid Search sought to relieve the latter limitation by providing an improved class annotation technique. Herein, we demonstrate the importance of using a precise mass spectral reconstruction technique to increase the confidence of detecting the presence of known-unknowns and subsequently identifying them successfully with NIST Hybrid Search function. We compared the AMDIS algorithm against the rBTEM algorithm and found that the latter could more accurately reconstruct the mass spectra of co-eluting known-unknown components. This has a far-reaching implication to increase the number of identified compounds in GC/MS scan data.

中文翻译：

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使用准确重建的质谱改进已知-未知数的注释

摘要 使用气相色谱/质谱 (GC/MS) 全谱采集模式进行化学分析通常会发现已知和未知的成分。这些是由于数据处理方法的限制或质谱库的广度有限而引起的未识别成分。最近引入的 NIST 混合搜索试图通过提供改进的类注释技术来缓解后一种限制。在此，我们证明了使用精确的质谱重建技术来提高检测已知未知物的存在并随后使用 NIST 混合搜索功能成功识别它们的置信度的重要性。我们将 AMDIS 算法与 rBTEM 算法进行了比较，发现后者可以更准确地重建共流出已知-未知组分的质谱图。这对于增加 GC/MS 扫描数据中已识别化合物的数量具有深远的意义。