Alon Tal, Amirav Aviv
School of Chemistry, Tel Aviv University, Tel Aviv, 6997801, Israel.
Afeka Academic College of Engineering, Tel Aviv, 6998812, Israel.
Rapid Commun Mass Spectrom. 2015 Dec 15;29(23):2287-92. doi: 10.1002/rcm.7392.
Library-based compound identification with electron ionization (EI) mass spectrometry (MS) is a well-established identification method which provides the names and structures of sample compounds up to the isomer level. The library (such as NIST) search algorithm compares different EI mass spectra in the library's database with the measured EI mass spectrum, assigning each of them a similarity score called 'Match' and an overall identification probability. Cold EI, electron ionization of vibrationally cold molecules in supersonic molecular beams, provides mass spectra with all the standard EI fragment ions combined with enhanced Molecular Ions and high-mass fragments. As a result, Cold EI mass spectra differ from those provided by standard EI and tend to yield lower matching scores. However, in most cases, library identification actually improves with Cold EI, as library identification probabilities for the correct library mass spectra increase, despite the lower matching factors.
This research examined the way that enhanced molecular ion abundances affect library identification probability and the way that Cold EI mass spectra, which include enhanced molecular ions and high-mass fragment ions, typically improve library identification results. It involved several computer simulations, which incrementally modified the relative abundances of the various ions and analyzed the resulting mass spectra.
The simulation results support previous measurements, showing that while enhanced molecular ion and high-mass fragment ions lower the matching factor of the correct library compound, the matching factors of the incorrect library candidates are lowered even more, resulting in a rise in the identification probability for the correct compound.
This behavior which was previously observed by analyzing Cold EI mass spectra can be explained by the fact that high-mass ions, and especially the molecular ion, characterize a compound more than low-mass ions and therefore carries more weight in library search identification algorithms. These ions are uniquely abundant in Cold EI, which therefore enables enhanced compound characterization along with improved NIST library based identification.
基于库的电子电离(EI)质谱(MS)化合物鉴定是一种成熟的鉴定方法,可提供直至异构体水平的样品化合物名称和结构。库(如NIST)搜索算法将库数据库中的不同EI质谱与测得的EI质谱进行比较,为每个质谱分配一个称为“匹配度”的相似性得分和一个总体鉴定概率。冷EI,即超声分子束中振动冷分子的电子电离,可提供包含所有标准EI碎片离子以及增强的分子离子和高质量碎片的质谱。因此,冷EI质谱与标准EI提供的质谱不同,并且往往产生较低的匹配得分。然而,在大多数情况下,尽管匹配因子较低,但随着正确库质谱的库鉴定概率增加,使用冷EI时库鉴定实际上会得到改善。
本研究考察了增强的分子离子丰度影响库鉴定概率的方式,以及包含增强的分子离子和高质量碎片离子的冷EI质谱通常改善库鉴定结果的方式。研究涉及多个计算机模拟,这些模拟逐步改变各种离子的相对丰度并分析所得质谱。
模拟结果支持先前的测量,表明虽然增强的分子离子和高质量碎片离子降低了正确库化合物的匹配因子,但不正确库候选物的匹配因子降低得更多,从而导致正确化合物的鉴定概率上升。
先前通过分析冷EI质谱观察到的这种行为可以通过以下事实来解释,即高质量离子,尤其是分子离子,比低质量离子更能表征化合物,因此在库搜索鉴定算法中权重更大。这些离子在冷EI中独特地丰富,因此能够增强化合物表征以及改进基于NIST库的鉴定。