The use of ion mobility separations (IMSs) in metabolomics approaches has started to be deeply explored in the last years. In this work, the use of liquid chromatography (LC) coupled to IMS‐quadrupole time‐of‐flight mass spectrometry (QTOF MS) has been evaluated in a metabolomics experiments using single injection of the samples. IMS has allowed obtaining cleaner fragmentation spectra, of nearly tandem MS quality, in data‐independent acquisition mode. This is much useful in this research area as a second injection, generally applied in LC‐QTOF MS workflows to obtain tandem mass spectra, is not necessary, saving time and evading possible compound degradation. As a case study, the smoke produced after combustion of herbal blends used to spray synthetic cannabinoids has been selected as study matrix. The smoke components were trapped in carbon cartridges, desorbed and analyzed by LC‐IMS‐QTOF MS using different separation mechanisms (reversed phase and HILIC) and acquiring in both positive and negative mode to widen the chemical domain. Partial Least Squares–Discriminant Analysis highlighted several compounds, and ratio between N‐Isopropyl‐3‐(isoquinolinyl)‐2‐propen‐1‐amine and quinoline allowed differentiating between tobacco and herbal products. These two compounds were tentatively identified using the cleaner fragmentation spectra from a single injection in the IMS‐QTOF MS, with additional confidence obtained by retention time (Rt) and collisional cross section (CCS) prediction using artificial neural networks. Data from this work show that LC‐IMS‐QTOF is an efficient technique in untargeted metabolomics, avoiding re‐injection of the samples for elucidation purposes. In addition, the prediction models for Rt and CCS resulted of help in the elucidation process of potential biomarkers.

中文翻译：

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在代谢组学研究中使用离子淌度-高分辨率质谱法可在单次进样中提供接近MS / MS的质量数据

近年来，在代谢组学方法中离子迁移分离（IMSs）的使用已开始深入研究。在这项工作中，已通过使用一次进样的代谢组学实验评估了液相色谱（LC）与IMS四极杆飞行时间质谱（QTOF MS）结合使用的情况。IMS允许在独立于数据的采集模式下获得接近串联质谱质量的更清晰的碎片质谱图。这在本研究领域非常有用，因为通常不需要在LC-QTOF MS工作流程中使用第二次进样来获得串联质谱，从而节省了时间并避免了可能的化合物降解。作为案例研究，已选择燃烧用于喷洒合成大麻素的草药混合物燃烧后产生的烟气作为研究基质。烟气成分被捕集在碳盒中，然后使用不同的分离机制（反相和HILIC）通过LC-IMS-QTOF MS进行解吸和分析，并以正负两种方式采集以扩大化学范围。偏最小二乘判别分析突出显示了几种化合物，N-异丙基-3-（异喹啉基）-2-丙-1-胺与喹啉的比例可以区分烟草和草药产品。使用IMS‐QTOF MS中单次进样的更清晰的碎片谱图初步鉴定了这两种化合物，并通过使用人工神经网络的保留时间（Rt）和碰撞截面（CCS）预测获得了更高的置信度。这项工作的数据表明，LC‐IMS‐QTOF是一种有效的无目标代谢组学技术，避免出于说明目的重新注入样品。此外，Rt和CCS的预测模型有助于阐明潜在生物标记物。