Department of Chemistry, Sharif University of Technology, P.O. Box 11155-9516, Tehran, Iran; Institute for Instrumental Analytics and Bioanalytics, Mannheim University of Applied Sciences, 68163, Mannheim, Germany.
Institute for Instrumental Analytics and Bioanalytics, Mannheim University of Applied Sciences, 68163, Mannheim, Germany.
Anal Chim Acta. 2024 Feb 8;1289:342204. doi: 10.1016/j.aca.2024.342204. Epub 2024 Jan 2.
Gas chromatography-ion mobility spectrometry (GC-IMS) is a powerful analytical technique which has gained widespread use in a variety of fields. Detecting peaks in GC-IMS data is essential for chemical identification. Topological data analysis (TDA) has the ability to record alterations in topology throughout the entire spectrum of GC-IMS data and retain this data in diagrams known as persistence diagrams. To put it differently, TDA naturally identifies characteristics such as mountains, volcanoes, and their higher-dimensional equivalents within the original data and measures their significance.
In the present contribution, a novel approach based on persistent homology (a flagship technique of TDA) is suggested for automatic 2D peak detection in GC-IMS. For this purpose, two different GC-IMS data examples (urine and olive oil) are used to show the performance of the proposed method. The outputs of the algorithm are GC-IMS chromatogram with detected peaks, persistence plot showing the importance (intensity) of the detected peaks and a table with retention times (RT), drift times (DT), and persistence scores of detected peaks. The RT and DT can be used for identification of the peaks and persistence scores for quantitation. Additionally, watershed segmentation is applied to the GC-IMS images to index individual peaks and segment overlapping compounds allowing for a more accurate identification and quantification of individual peaks.
Inspection of the results for GC-IMS datasets showed the accurate and reliable performance of the proposed strategy based on persistent homology for automatic 2D GC-IMS peak detection for qualitative and quantitative analysis. In addition, this approach can be easily extended to other types of hyphenated chromatographic and/or spectroscopic data.
气相色谱-离子迁移谱（GC-IMS）是一种强大的分析技术，已在许多领域得到广泛应用。检测 GC-IMS 数据中的峰对于化学鉴定至关重要。拓扑数据分析（TDA）有能力记录整个 GC-IMS 数据谱中的拓扑变化，并将这些数据保留在称为持久性图的图中。换句话说，TDA 自然地识别原始数据中的山脉、火山及其更高维等效物等特征，并测量它们的重要性。
本研究提出了一种基于持久同调（TDA 的标志性技术）的新型方法，用于自动进行 GC-IMS 中的二维峰检测。为此，使用了两个不同的 GC-IMS 数据示例（尿液和橄榄油）来展示所提出方法的性能。算法的输出是带有检测到的峰的 GC-IMS 色谱图、显示检测到的峰的重要性（强度）的持久性图以及带有检测到的峰的保留时间（RT）、漂移时间（DT）和持久性得分的表。RT 和 DT 可用于鉴定峰，而持久性得分可用于定量。此外，还对 GC-IMS 图像应用了分水岭分割，以索引各个峰并分割重叠化合物，从而实现对各个峰的更准确鉴定和定量。
对 GC-IMS 数据集的结果进行检查表明，基于持久同调的自动二维 GC-IMS 峰检测策略在定性和定量分析中具有准确可靠的性能。此外，该方法可以轻松扩展到其他类型的联用色谱和/或光谱数据。
