R. Lukmanov, C. D. de Koning, Peter Keresztes Schmidt, D. Wacey, N. Ligterink, S. Gruchola, V. Grimaudo, A. Neubeck, A. Riedo, M. Tulej, P. Wurz
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引用次数: 1
摘要
从非结构化、大型和复杂的质谱信号中提取有用的信息是质谱分析许多应用领域的挑战。因此,需要新的数据分析方法来帮助揭示这些信号的复杂性。在本文中,我们使用新开发的高质量分辨率激光电离质谱仪(fs- lms - gt)检测了1.88 Ga Gunflint燧石的化学成分。我们报告了以下结果:1)Gunflint燧石样品的质谱多元素成像;2)利用非线性降维和光谱相似网络从空间质谱数据中识别多种化学实体。对4万份质谱的分析表明,该物质存在化学非均质性(7种微量化合物)和两个大的光谱团簇,分别来自有机物质和无机寄主矿物。我们的研究结果显示了fs-LIMS成像与多种学习方法相结合在研究化学多样性样品中的效用。
High Mass Resolution fs-LIMS Imaging and Manifold Learning Reveal Insight Into Chemical Diversity of the 1.88 Ga Gunflint Chert
Extraction of useful information from unstructured, large and complex mass spectrometric signals is a challenge in many application fields of mass spectrometry. Therefore, new data analysis approaches are required to help uncover the complexity of such signals. In this contribution, we examined the chemical composition of the 1.88 Ga Gunflint chert using the newly developed high mass resolution laser ionization mass spectrometer (fs-LIMS-GT). We report results on the following: 1) mass-spectrometric multi-element imaging of the Gunflint chert sample; and 2) identification of multiple chemical entities from spatial mass spectrometric data utilizing nonlinear dimensionality reduction and spectral similarity networks. The analysis of 40′000 mass spectra reveals the presence of chemical heterogeneity (seven minor compounds) and two large clusters of spectra registered from the organic material and inorganic host mineral. Our results show the utility of fs-LIMS imaging in combination with manifold learning methods in studying chemically diverse samples.