Ripszam Matyas, Bruderer Tobias, Biagini Denise, Ghimenti Silvia, Lomonaco Tommaso, Di Francesco Fabio
University of Pisa, Department of Chemistry and Industrial Chemistry, via Giuseppe Moruzzi 13, 56124 Pisa, Italy.
University of Pisa, Department of Chemistry and Industrial Chemistry, via Giuseppe Moruzzi 13, 56124 Pisa, Italy.
Talanta. 2023 May 15;257:124333. doi: 10.1016/j.talanta.2023.124333. Epub 2023 Feb 8.
A key issue in GCxGC-HRMS data analysis is how to approach large-sample studies in an efficient and comprehensive way. We have developed a semi-automated data-driven workflow from identification to suspect screening, which allows highly selective monitoring of each identified chemical in a large-sample dataset. The example dataset used to illustrate the potential of the approach consisted of human sweat samples from 40 participants, including field blanks (80 samples). These samples have been collected in a Horizon 2020 project to investigate the capacity of body odour to communicate emotion and influence social behaviour. We used dynamic headspace extraction, which allows comprehensive extraction with high preconcentration capability, and has to date only been used for a few biological applications. We were able to detect a set of 326 compounds from a diverse range of chemical classes (278 identified compounds, 39 class unknowns, and 9 true unknowns). Unlike partitioning-based extraction methods, the developed method detects semi-polar (log P < 2) nitrogen and oxygen-containing compounds. However, it is unable to detect certain acids due to the pH conditions of unmodified sweat samples. We believe that our framework will open up the possibility of efficiently using GCxGC-HRMS for large-sample studies in a wide range of applications such as biological and environmental studies.
气相色谱-高分辨质谱（GCxGC-HRMS）数据分析中的一个关键问题是如何以高效且全面的方式处理大样本研究。我们开发了一种从鉴定到可疑物筛查的半自动数据驱动工作流程，该流程允许在大样本数据集中对每种鉴定出的化学物质进行高选择性监测。用于说明该方法潜力的示例数据集由40名参与者的人类汗液样本组成，包括现场空白样本（80个样本）。这些样本是在一个“地平线2020”项目中收集的，旨在研究体味传达情感和影响社会行为的能力。我们使用了动态顶空萃取法，该方法具有高预浓缩能力，能够进行全面萃取，并且迄今为止仅用于少数生物应用。我们能够从多种化学类别中检测出一组326种化合物（278种已鉴定化合物、39种类别未知物和9种真正未知物）。与基于分配的萃取方法不同，所开发的方法能够检测半极性（log P < 2）含氮和含氧化合物。然而，由于未改性汗液样本的pH条件，它无法检测某些酸类。我们相信，我们的框架将为在生物和环境研究等广泛应用中高效使用GCxGC-HRMS进行大样本研究开辟可能性。
