Department of Internal Medicine , University of California, Davis , 4150 V Street, Suite 3400 , Sacramento , California 95817 , United States.
VA Northern California Health Care System , 10535 Hospital Way , Mather , California 95655 , United States.
Anal Chem. 2019 Aug 20;91(16):10509-10517. doi: 10.1021/acs.analchem.9b01428. Epub 2019 Jul 29.
Gas-phase trace chemical detection techniques such as ion mobility spectrometry (IMS) and differential mobility spectrometry (DMS) can be used in many settings, such as evaluating the health condition of patients or detecting explosives at airports. These devices separate chemical compounds in a mixture and provide information to identify specific chemical species of interest. Further, these types of devices operate well in both controlled lab environments and in-field applications. Frequently, the commercial versions of these devices are highly tailored for niche applications (e.g., explosives detection) because of the difficulty involved in reconfiguring instrumentation hardware and data analysis software algorithms. In order for researchers to quickly adapt these tools for new purposes and broader panels of chemical targets, it is critical to develop new algorithms and methods for generating libraries of these sensor responses. Microelectromechanical system (MEMS) technology has been used to fabricate DMS devices that miniaturize the platforms for easier deployment; however, concurrent advances in advanced data analytics are lagging. DMS generates complex three-dimensional dispersion plots for both positive and negative ions in a mixture. Although simple spectra of single chemicals are straightforward to interpret (both visually and via algorithms), it is exceedingly challenging to interpret dispersion plots from complex mixtures with many chemical constituents. This study uses image processing and computer vision steps to automatically identify features from DMS dispersion plots. We used the bag-of-words approach adapted from natural language processing and information retrieval to cluster and organize these features. Finally, a support vector machine (SVM) learning algorithm was trained using these features in order to detect and classify specific compounds in these represented conceptualized data outputs. Using this approach, we successfully maintain a high level of correct chemical identification, even when a gas mixture increases in complexity with interfering chemicals present.
气相痕量化学检测技术，如离子迁移谱（IMS）和差分迁移谱（DMS），可用于多种场合，如评估患者的健康状况或在机场检测爆炸物。这些设备可以分离混合物中的化合物，并提供识别特定感兴趣化学物质的信息。此外，这些类型的设备在受控的实验室环境和现场应用中都能很好地运行。通常，这些设备的商业版本针对特定应用（例如爆炸物检测）进行了高度定制，因为重新配置仪器硬件和数据分析软件算法具有一定的难度。为了使研究人员能够快速将这些工具用于新的目的和更广泛的化学目标面板，开发用于生成这些传感器响应库的新算法和方法至关重要。微机电系统（MEMS）技术已用于制造 DMS 设备，这些设备可将平台小型化，便于部署；然而，先进数据分析的同步进展却落后了。DMS 为混合物中的正离子和负离子生成复杂的三维色散图。尽管混合物中许多化学物质的复杂色散图难以解释，但单种化学物质的简单光谱（无论是通过视觉还是通过算法）都很容易解释。本研究使用图像处理和计算机视觉步骤从 DMS 色散图中自动识别特征。我们使用了从自然语言处理和信息检索中改编的词袋方法来对这些特征进行聚类和组织。最后，使用这些特征训练支持向量机（SVM）学习算法，以便在这些代表概念化数据输出中检测和分类特定化合物。使用这种方法，即使在存在干扰化学物质的情况下，气体混合物的复杂性增加，我们也能成功地保持高度正确的化学识别率。
