Harvesting information from chromatographic data

从色谱数据中获取信息

• 批准号: 355800-2011
• 负责人: Harynuk, James
• 金额: $ 1.82万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2012
• 资助国家: 加拿大
• 起止时间: 2012-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=507105
• 关键词: Harvesting; information; chromatographic; data

In all walks of life there are complex samples which must be studied. These samples can comprise hundreds (if not thousands) of different chemicals and are found in situations that range from identifying and measuring unsaturated fatty acids in fish oils and foods, to tracking changes in the composition of petrochemical samples during refining processes, to screening environmental samples for known and/or emerging pollutants, or profiling metabolites in the body to identify a disease state. Powerful tools such as gas chromatography/mass spectrometry (GC/MS) and comprehensive two-dimensional gas chromatography (GCxGC) are used to perform many of these measurements. With these techniques, researchers can separate and analyze incredibly complex samples. Two challenges with these techniques are ascertaining the identity of the compounds that have been separated, and efficiently transforming the incredibly detailed data the instruments provide into usable information from which a decision can be made. The proposed research program addresses both of these issues. First through study of the chemistry governing the separation process, and second through the development of mathematical and computational tools that efficiently extract information from the data. Through modeling the properties that govern the separation process and linking those back to the structures of molecules, we will develop a new tool to aid in the identification of unknown molecules. Our new mathematical tools work with these methods to rapidly interpret the data and generate useful information about a set of samples.

各行各业都有需要研究的复杂样本。这些样品可包含数百种(如果不是数千种)不同的化学物质，可在各种情况下发现，从识别和测量鱼油和食品中的不饱和脂肪酸，到跟踪精炼过程中石化样品成分的变化，到筛选环境样品中已知和/或新出现的污染物，或分析体内代谢物以确定疾病状态。使用了诸如气相色谱/质谱仪(GC/MS)和全二维气相色谱(GCxGC)等强大的工具来执行其中的许多测量。使用这些技术，研究人员可以分离和分析令人难以置信的复杂样本。这些技术的两个挑战是确定被分离的化合物的身份，以及有效地将仪器提供的令人难以置信的详细数据转换为可用于做出决定的信息。拟议的研究计划解决了这两个问题。首先是通过研究控制分离过程的化学物质，其次是通过开发能够有效地从数据中提取信息的数学和计算工具。通过对控制分离过程的属性进行建模，并将这些属性与分子结构联系起来，我们将开发一种新的工具来帮助识别未知分子。我们的新数学工具与这些方法一起工作，以快速解释数据并生成关于一组样本的有用信息。