Hoggard Jamin C, Synovec Robert E
Department of Chemistry, Box 351700, University of Washington, Seattle, Washington 98195-1700, USA.
Anal Chem. 2008 Sep 1;80(17):6677-88. doi: 10.1021/ac800624e. Epub 2008 Aug 2.
We previously reported a method for the automated (objective) selection of a PARAFAC model having an appropriate number of factors for mathematical resolution of signal from a target analyte in GC x GC-TOFMS data (i.e., for an analysis in which the identity of the analyte is known a priori). While the previous target method has been successfully applied in several studies, the target method requires that the identity of the analyte be known. Also, multiple applications of the target method are required in cases where several analytes of interest are present in a single subsection of the chromatogram. Thus, having to know the analyte identity a priori restricts the applicability of the automated implementation of PARAFAC. The method presented in this report generalizes the previous method to allow analysis of one or more nontarget analyte signals in a subsection of a GC x GC-TOFMS chromatogram (i.e., for analyses when identities of analyte and interferences are not known a priori), thereby addressing and overcoming the limitations of the target method. Herein, we put the nontarget analyte PARAFAC method into theoretical context and illustrate the mechanics of the method using simulated data. We use real experimental GC x GC-TOFMS data to demonstrate the broad applicability of the method, with various analysis situations selected to illustrate challenging chemical analysis scenarios.
我们之前报道了一种方法，用于自动（客观）选择具有适当因子数的PARAFAC模型，以便从GC x GC-TOFMS数据中对目标分析物的信号进行数学解析（即，用于事先已知分析物身份的分析）。虽然之前的目标方法已在多项研究中成功应用，但该目标方法要求分析物的身份是已知的。此外，在色谱图的单个子区域中存在多种感兴趣的分析物的情况下，需要多次应用目标方法。因此，必须事先知道分析物身份限制了PARAFAC自动实现的适用性。本报告中提出的方法对先前的方法进行了推广，以允许对GC x GC-TOFMS色谱图的一个子区域中的一个或多个非目标分析物信号进行分析（即，用于事先不知道分析物和干扰物身份的分析），从而解决并克服了目标方法的局限性。在此，我们将非目标分析物PARAFAC方法置于理论背景中，并使用模拟数据说明该方法的原理。我们使用实际的实验GC x GC-TOFMS数据来证明该方法的广泛适用性，选择了各种分析情况以说明具有挑战性的化学分析场景。
