Arndt Daniel, Wachsmuth Christian, Buchholz Christoph, Bentley Mark
PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000, Neuchâtel, Switzerland.
Rapid Commun Mass Spectrom. 2020 Jan 30;34(2):e8571. doi: 10.1002/rcm.8571.
For the characterization of the chemical composition of complex matrices such as tobacco smoke, containing more than 6000 constituents, several analytical approaches have to be combined to increase compound coverage across the chemical space. Furthermore, the identification of unknown molecules requiring the implementation of additional confirmatory tools in the absence of reference standards, such as tandem mass spectrometry spectra comparisons and in silico prediction of mass spectra, is a major bottleneck.
We applied a combination of four chromatographic/ionization techniques (reversed-phase (RP) - heated electrospray ionization (HESI) in both positive (+) and negative (-) modes, RP - atmospheric pressure chemical ionization (APCI) in positive mode, and hydrophilic interaction liquid chromatography (HILIC) - HESI positive) using a Thermo Q Exactive™ liquid chromatography/high-resolution accurate mass spectrometry (LC/HRAM-MS) platform for the analysis of 3R4F-derived smoke. Compound identification was performed by using mass spectral libraries and in silico predicted fragments from multiple integrated databases.
A total of 331 compounds with semi-quantitative estimates ≥100 ng per cigarette were identified, which were distributed within the known chemical space of tobacco smoke. The integration of multiple LC/HRAM-MS-based chromatographic/ionization approaches combined with complementary compound identification strategies was key for maximizing the number of amenable compounds and for strengthening the level of identification confidence. A total of 50 novel compounds were identified as being present in tobacco smoke. In the absence of reference MS spectra, in silico MS spectra prediction gave a good indication for compound class and was used as an additional confirmatory tool for our integrated non-targeted screening (NTS) approach.
This study presents a powerful chemical characterization approach that has been successfully applied for the identification of novel compounds in cigarette smoke. We believe that this innovative approach has general applicability and a huge potential benefit for the analysis of any complex matrices.
对于诸如烟草烟雾这种含有6000多种成分的复杂基质的化学成分表征，必须结合多种分析方法以提高化学空间中化合物的覆盖范围。此外，在没有参考标准品的情况下鉴定未知分子需要实施额外的确认工具，如串联质谱光谱比较和质谱的计算机模拟预测，这是一个主要瓶颈。
我们使用Thermo Q Exactive™液相色谱/高分辨率精确质谱（LC/HRAM-MS）平台，应用四种色谱/电离技术的组合（反相（RP）-正（+）负（-）模式下的加热电喷雾电离（HESI）、正模式下的RP-大气压化学电离（APCI）以及亲水作用液相色谱（HILIC）-HESI正模式）来分析3R4F衍生烟雾。通过使用质谱库和来自多个综合数据库的计算机模拟预测碎片进行化合物鉴定。
共鉴定出331种半定量估计值≥每支香烟100 ng的化合物，它们分布在烟草烟雾的已知化学空间内。基于多种LC/HRAM-MS的色谱/电离方法与互补的化合物鉴定策略相结合，是最大化可分析化合物数量和增强鉴定置信水平的关键。共鉴定出50种烟草烟雾中存在的新型化合物。在没有参考质谱图的情况下，计算机模拟质谱预测为化合物类别提供了良好指示，并用作我们综合非靶向筛选（NTS）方法的额外确认工具。
本研究提出了一种强大的化学表征方法，已成功应用于香烟烟雾中新型化合物的鉴定。我们相信这种创新方法具有普遍适用性，对任何复杂基质的分析都有巨大的潜在益处。
