Comparative Visualization and Analysis for GCxGC

描述（由申请人提供）：本项目将研究和开发基于计算机的比较可视化和交互式分析的方法，由全面的二维气相色谱（GCxGC）生成的复杂数据。GCxGC是一种新兴技术，其分离能力比传统GC提高了一个数量级。今天，世界各地的一些先进实验室正在开拓GCxGC的各种应用，如空气，土壤，食品和水的暴露概况的环境分析，以及用于识别和定量血液，尿液，牛奶和呼吸样本中有毒产品的医疗保健分析。这些应用中的许多分析需要对样品进行详细的化学比较，例如，将新样品与先前样品进行比较，以检测和监测变化，或将测试样品与参考样品进行比较，以确定和量化化学成分的差异。GCxGC是一种强大的比较分析技术。GCxGC面临的主要挑战是难以分析和解释其生成的大型复杂数据。GCxGC数据的数量和复杂性需要研究和开发新的信息技术。该项目将开发和演示GCxGC数据比较可视化和交互式分析的方法和工具。GCxGC标量场比较分析的可视化将使用模型-视图-控制器（MVC）架构中的多视图多层（MVML）框架进行开发。在MVML框架内，将开发几种不同的可视化策略，利用彩色化和动画。预期的结果将提供一个基于计算机的系统开发的基础比较GCxGC分析复杂的样品，并将推进知识的框架和方法的可视化和交互式分析GCxGC数据和其他二维标量场。

DESCRIPTION (provided by applicant): This project will investigate and develop methods for computer-based comparative visualization and interactive analysis of complex data generated by comprehensive two-dimensional gas chromatography (GCxGC). GCxGC is an emerging technology that provides an order-of-magnitude increase in separation capacity over traditional GC. Today, a few advanced laboratories around the world are pioneering GCxGC for a variety of applications such as environmental analyses of exposure profiles in air, soil, food, and water, and health-care analyses for the identification and quantification of toxic products in blood, urine, milk, and breath samples. Many analyses in these applications require detailed chemical comparisons of samples, e.g., the comparison of a new sample with a previous sample to detect and monitor changes or the comparison of a test sample with a reference sample to identify and quantify differences in chemical composition. GCxGC is a powerful technology for comparative analyses. The principal challenge for GCxGC is the difficulty of analyzing and interpreting the large, complex data it generates. The quantity and complexity of GCxGC data necessitates the investigation and development of new information technologies. This project will develop and demonstrate methods and tools for comparative visualization and interactive analysis of GCxGC data. Visualizations for comparative analyses of GCxGC scalar fields will be developed using a Multi-View Multi-Layer (MVML) framework in a Model-View-Controller (MVC) architecture. Within the MVML framework, several different strategies for visualization will be developed, utilizing colorization and animation. The expected results will provide a foundation for development of a computer-based system for comparative GCxGC analyses of complex samples and will advance knowledge of frameworks and methods for visualization and interactive analysis of GCxGC data and other two-dimensional scalar fields.