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

合作研究：SEI：比较可视化和分析的信息模型 - 综合二维气相色谱信息学

• 批准号: 0431252
• 负责人: Jean-Marie Dimandja
• 金额: $ 11.87万
• 依托单位: Spelman College
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2007-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0431252&HistoricalAwards=false
• 关键词: Collaborative; Research; SEI; Information; Modeling

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.

该项目正在研究和开发信息模型，用于比较可视化和分析综合二维气相色谱(GCxGC)产生的复杂化学分离。GCxGC是一项功能强大的新技术，其分离能力比传统GC提高了数量级。一些先进的实验室正在将GCxGC用于各种应用，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产生的大量、复杂的数据。该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。正在开发分析方法，以从GCxGC强度数据和GCxGC-MS(GCxGC-MS)数据的结构特征中提取模型组分。这些模型支持对不同的化学浓度进行校准，并对数据的拟合质量进行测量。GCxGC信息模型的比较可视化采用了多种查看策略，并带有彩色化和动画，为用户提供了多种交互分析的途径。交互式分析支持与视图导航和控制、基于模型的信息和元数据的查询和编辑以及结构化报告的比较。基于模型的信息技术有助于在广泛范围的问题中进行高级化学分析。该项目正在展示它们在GCxGC的两个应用中的价值。一个应用程序分析了石油泄漏后环境中复杂的石油碳氢化合物混合物的变化。另一个应用程序使用GCxGC对香水进行指纹识别，以进行分类和识别。