Tandem mass spectrometry (MS/MS) is a primary tool for the identification of small molecules and metabolites where resultant spectra are most commonly identified by matching them with spectra in MS/MS reference libraries. The high degree of variability in MS/MS spectrum acquisition techniques and parameters creates a significant challenge for building standardized reference libraries. Here we present a method to improve the usefulness of existing MS/MS libraries by augmenting available experimental spectra data sets with statistically interpolated spectra at unreported collision energies. We find that highly accurate spectral approximations can be interpolated from as few as three experimental spectra and that the interpolated spectra will be consistent with true spectra gathered from the same instrument as the experimental spectra. Supplementing existing spectral databases with interpolated spectra yields consistent improvements to identification accuracy on a range of instruments and precursor types. Applying this method yields significant improvements (∼10% more spectra correctly identified) on large data sets (2000–10 000 spectra), indicating this is a quick yet adept tool for improving spectral matching in situations where available reference libraries are not yet sufficient. We also find improvements of matching spectra across instrument types (between an Agilent Q-TOF and an Orbitrap Elite), at high collision energies (50–90 eV), and with smaller data sets available through MassBank.

中文翻译：

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使用光谱插值增强 MS/MS 库以改进识别

串联质谱 (MS/MS) 是鉴定小分子和代谢物的主要工具，其中产生的光谱最常通过将它们与 MS/MS 参考库中的光谱匹配来识别。MS/MS 光谱采集技术和参数的高度可变性为构建标准化参考库带来了重大挑战。在这里，我们提出了一种方法，通过在未报告的碰撞能量下使用统计插值光谱来增加可用的实验光谱数据集，从而提高现有 MS/MS 库的实用性。我们发现高度准确的光谱近似值可以从少至三个实验光谱中进行插值，并且插值后的光谱将与从与实验光谱相同的仪器中收集到的真实光谱一致。用插值光谱补充现有光谱数据库可以持续提高一系列仪器和前体类型的识别准确度。应用此方法可在大型数据集（2000-10 000 个光谱）上产生显着改进（正确识别光谱增加约 10%），表明这是在可用参考库还不够的情况下改进光谱匹配的快速而熟练的工具。我们还发现，在高碰撞能量 (50–90 eV) 和 MassBank 提供的较小数据集下，不同仪器类型（Agilent Q-TOF 和 Orbitrap Elite 之间）的匹配光谱得到了改进。应用此方法可在大型数据集（2000-10 000 个光谱）上产生显着改进（正确识别光谱增加约 10%），表明这是在可用参考库还不够的情况下改进光谱匹配的快速而熟练的工具。我们还发现，在高碰撞能量 (50–90 eV) 和 MassBank 提供的较小数据集下，不同仪器类型（Agilent Q-TOF 和 Orbitrap Elite 之间）的匹配光谱得到了改进。应用此方法可在大型数据集（2000-10 000 个光谱）上产生显着改进（正确识别光谱增加约 10%），表明这是在可用参考库还不够的情况下改进光谱匹配的快速而熟练的工具。我们还发现，在高碰撞能量 (50–90 eV) 和 MassBank 提供的较小数据集下，不同仪器类型（Agilent Q-TOF 和 Orbitrap Elite 之间）的匹配光谱得到了改进。