Sinha Amanda E, Fraga Carlos G, Prazen Bryan J, Synovec Robert E
Department of Chemistry, Centerfor Process Analytical Chemistry, Box 351700, University of Washington, Seattle, WA 98195-1700, USA.
J Chromatogr A. 2004 Feb 20;1027(1-2):269-77. doi: 10.1016/j.chroma.2003.08.081.
Two-dimensional comprehensive gas chromatography (GC x GC) is a powerful instrumental tool in its own right that can be used to analyze complex mixtures, generating selective data that is applicable to multivariate quantitative analysis and pattern recognition. It has been recently demonstrated that by coupling GC x GC to time-of-flight mass spectrometry (TOFMS), a highly selective technique is produced. One separation on a GC x GC/TOFMS provides retention times on two chromatographic columns and a complete mass spectrum for each component within the mixture. In this manuscript, we demonstrate how the selectivity of GC x GC/TOFMS combined with trilinear chemometric techniques such as trilinear decomposition (TLD) and parallel factor analysis (PARAFAC) results in a powerful analytical methodology. Using TLD and PARAFAC, partially resolved components in complex mixtures can be deconvoluted and identified using only one data set without requiring either signal shape assumptions or fully selective mass signals. Specifically, a region of overlapped peaks in a complex environmental sample was mathematically resolved with TLD and PARAFAC to demonstrate the utility of these techniques as applied to GC x GC/TOFMS data of a complex mixture. For this data, it was determined that PARAFAC initiated by TLD performed a better deconvolution than TLD alone. After deconvolution, mass spectral profiles were then matched to library spectra for identification. A standard addition analysis was performed on one of the deconvoluted analytes to demonstrate the utility of TLD-initiated PARAFAC for quantification without the need for accurate retention time alignment between sample and standard data sets.
二维全二维气相色谱(GC×GC)本身就是一种强大的仪器分析工具,可用于分析复杂混合物,生成适用于多变量定量分析和模式识别的选择性数据。最近有研究表明,将GC×GC与飞行时间质谱(TOFMS)联用,可产生一种高选择性技术。在GC×GC/TOFMS上进行一次分离就能提供两个色谱柱上的保留时间以及混合物中每个组分的完整质谱图。在本论文中,我们展示了GC×GC/TOFMS的选择性与三线化学计量学技术(如三线分解法(TLD)和平行因子分析(PARAFAC))相结合,如何形成一种强大的分析方法。使用TLD和PARAFAC,复杂混合物中部分分离的组分仅通过一个数据集就能进行反卷积和识别,无需信号形状假设或完全选择性的质谱信号。具体而言,利用TLD和PARAFAC对复杂环境样品中重叠峰的区域进行了数学解析,以证明这些技术应用于复杂混合物的GC×GC/TOFMS数据时的实用性。对于该数据,确定由TLD启动的PARAFAC比单独使用TLD能实现更好的反卷积。反卷积后,将质谱图与谱库中的光谱进行匹配以进行鉴定。对其中一种反卷积分析物进行了标准加入分析,以证明由TLD启动的PARAFAC在无需样品和标准数据集之间精确保留时间对齐的情况下进行定量分析的实用性。