Hoggard Jamin C, Synovec Robert E
Department of Chemistry, Box 351700, University of Washington, Seattle, Washington 98195-1700, USA.
Anal Chem. 2007 Feb 15;79(4):1611-9. doi: 10.1021/ac061710b.
PARAFAC (parallel factor analysis) is a powerful chemometric method that has been demonstrated as a useful deconvolution technique in dealing with data obtained using comprehensive two-dimensional gas chromatography combined with time-of-flight mass spectrometry (GC x GC-TOFMS). However, selection of a PARAFAC model having an appropriate number of factors can be challenging, especially at low S/N or for analytes in the presence of chromatographic and spectral overlapping compounds (interferences). Herein, we present a method for the automated selection of a PARAFAC model with an appropriate number of factors in GC x GC-TOFMS data, demonstrated for a target analyte of interest. The approach taken in the methodology is as follows. PARAFAC models are automatically generated having an incrementally higher number of factors until mass spectral matching of the corresponding loadings in the model against a target analyte mass spectrum indicates overfitting has occurred. Then, the model selected simply has one less factor than the overfit model. Results indicate this model selection approach is viable across the detection range of the instrument from overloaded analyte signal down to low S/N analyte signal (total ion current signal intensity at analyte peak maximum S/N < 1). While the methodology is generally applicable to comprehensive two-dimensional separations using multichannel spectral detection, we evaluated it with several target analytes using GC x GC-TOFMS. For brevity in this report, only results for bromobenzene as target analyte are presented. Alternatively, instead of using the model with one less factor than the overfit model, one can select the model with the highest mass spectral match for the target analyte from among all the models generated (excluding the overfit model). Both model selection approaches gave essentially identical results.
平行因子分析（PARAFAC）是一种强大的化学计量学方法，已被证明是一种有用的反卷积技术，可用于处理使用全二维气相色谱与飞行时间质谱联用（GC×GC - TOFMS）获得的数据。然而，选择具有适当因子数的PARAFAC模型可能具有挑战性，特别是在低信噪比或存在色谱和光谱重叠化合物（干扰物）的分析物的情况下。在此，我们提出了一种在GC×GC - TOFMS数据中自动选择具有适当因子数的PARAFAC模型的方法，并以感兴趣的目标分析物为例进行了演示。该方法采用的步骤如下。自动生成因子数逐渐增加的PARAFAC模型，直到模型中相应载荷与目标分析物质谱的质谱匹配表明出现过拟合。然后，选择的模型比过拟合模型少一个因子。结果表明，这种模型选择方法在仪器的检测范围内都是可行的，从过载分析物信号到低信噪比分析物信号（分析物峰最大值处的总离子流信号强度S/N < 1）。虽然该方法通常适用于使用多通道光谱检测的全二维分离，但我们使用GC×GC - TOFMS对几种目标分析物进行了评估。为了本报告的简洁性，仅给出了以溴苯为目标分析物的结果。或者，除了使用比过拟合模型少一个因子的模型外，还可以从所有生成的模型（不包括过拟合模型）中选择与目标分析物质谱匹配度最高的模型。两种模型选择方法得到的结果基本相同。
