Collaborative Research: SEI: Information Modeling for Comparative Visualizations and Analyses - Informatics for Comprehensive Two-Dimensional Gas Chromatography

该项目正在研究和开发信息模型，用于综合二维气相色谱（GCxGC）产生的复杂化学分离的比较可视化和分析。GCxGC是一项强大的新技术，与传统GC相比，它的分离能力提高了一个数量级。一些先进的实验室正在率先将GCxGC用于各种应用，例如环境监测和医疗保健，但研究人员缺乏先进的信息技术来分析和解释GCxGC生成的大量复杂数据。在这个项目中开发的模型抽象了基本的分析信息，用于比较可视化和分析。从GCxGC强度数据和质谱（GCxGC- ms）数据的结构特征中提取模型成分的分析方法正在开发中。该模型支持校准不同的化学浓度和测量的质量拟合的数据。GCxGC信息模型的比较可视化采用了多种查看策略，包括着色和动画，为用户提供了多种进行交互分析的途径。交互式分析支持与视图导航和控件的比较，基于模型的信息和元数据的查询和编辑，以及结构化报告。基于模型的信息技术促进了对广泛问题的高级化学分析。该项目展示了它们在GCxGC的两个应用程序中的价值。一项应用分析了石油泄漏后环境中石油碳氢化合物复杂混合物的变化。另一个应用程序使用GCxGC对香味进行指纹分类和识别。

This project is investigating and developing information modeling for comparative visualizations and analyses of complex chemical separations produced by comprehensive two-dimensional gas chromatography (GCxGC). GCxGC is a powerful new technology that provides an order-of-magnitude increase in separation capacity over traditional GC. A few advanced laboratories are pioneering GCxGC for a variety of applications, such as environmental monitoring and health-care, but researchers lack advanced information technologies for analyzing and interpreting the large, complex data generated by GCxGC.The models being developed in this project abstract essential analytical information for comparative visualization and analyses. Analytical methods are being developed to extract model components from the structural characteristics of both GCxGC intensity data and data from GCxGC with mass spectrometry (GCxGC-MS). The models support calibration for varying chemical concentrations and measurements of the quality of fit to the data. Comparative visualizations of the GCxGC information models employ multiple viewing strategies with colorization and animation to provide users with multiple avenues for interactive analyses. Interactive analyses support comparisons with view navigation and controls, queries and edits of model-based information and metadata, and structured reporting.The model-based information technologies facilitate advanced chemical analyses in a broad range of problems. The project is demonstrating their value for two applications of GCxGC. One application analyzes changes of complex mixtures of petroleum hydrocarbons in the environment following an oil spill. The other application uses GCxGC to fingerprint fragrances for classification and identification.