Harvesting information from chromatographic data

各行各业都有必须研究的复杂样本。这些样品可能包含数百种（如果不是数千种的话）不同的化学物质，可以在各种情况下发现，从鉴定和测量鱼油和食品中的不饱和脂肪酸，到跟踪炼油过程中石化样品的组成变化，到筛选已知和/或新出现的污染物的环境样品，或分析体内代谢物以确定疾病状态。强大的工具，如气相色谱/质谱（GC/MS）和全面的二维气相色谱（GCxGC）用于执行许多这些测量。有了这些技术，研究人员可以分离和分析极其复杂的样本。这些技术面临的两个挑战是确定已分离化合物的身份，以及有效地将仪器提供的令人难以置信的详细数据转化为可用的信息，从而做出决定。拟议的研究计划解决了这两个问题。首先通过研究控制分离过程的化学，其次通过开发数学和计算工具，有效地从数据中提取信息。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。

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.