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首页> 外文期刊>Journal of chromatography, A: Including electrophoresis and other separation methods >Simultaneous deconvolution and re-construction of primary and secondary overlapping peak clusters in comprehensive two-dimensional gas chromatography
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Simultaneous deconvolution and re-construction of primary and secondary overlapping peak clusters in comprehensive two-dimensional gas chromatography

机译：综合二维气相色谱法同时解卷积和重建一级和二级重叠峰簇

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摘要

In this study, simultaneous deconvolution and reconstruction of peak profiles in the first (~1D) and second dimension (~2D) of comprehensive two-dimensional (~2D) gas chromatography (GC×GC) is achieved on the basis of the property of this new type of instrumental data. First, selective information, where only one component contributes to the peak elution window of a given modulation event, is employed for stepwise stripping of each ~2D peak with the help of pure components corresponding to that compound from the neighbouring modulations. Simulation based on an exponentially modified Gaussian (EMG) model aids this process, where the EMG represents the envelope of all ~2D peaks for that compound. The peak parameters can be restricted by knowledge of the pure modulated ~2D GC peaks derived from the same primary compound, since it is modulated into several fractions during the trapping and re-focusing process of the cryogenic modulation system according to the modulation period. Next, relative areas of all pure ~2D components of that compound are considered for reconstruction of the primary peak. This strategy of exploitation of the additional information provided by the second dimension of separation allows effective deconvolution of GC×GC datasets. Non-linear least squares curve fitting (NLLSCF) allows the resolved ~2D chromatograms to be recovered. Accurate acquisition of the pure profiles in both ~1D and ~2D aids quantification of compositions and prediction of ~2D retention parameters, which are of interest for qualitative and quantitative analysis. The ratio between the sum of squares of deconvolution residual and original peak response (R_(rr)) is employed as an effective index to evaluate the resolution results. In this work, simulated and experimental examples are used to develop and test the proposed approach. Satisfactory performance for these studies is validated by minimum and maximum R_(rr) values of 1.34e-7% and 1.09e-2%; and 1.0e-3% and 3.0e~(-1)% for deconvolution of 1D and ~2D peaks, respectively. Results suggest that the present technique is suitable for GC×GC data processing.
机译：在这项研究中，根据二维色谱图（GC×GC）的特性，同时实现了第一维（〜1D）和二维（〜2D）色谱峰轮廓的同时解卷积和重构这种新型的仪器数据。首先，在只有一个成分对给定调制事件的峰洗脱窗口起作用的情况下，利用选择性信息，借助与来自相邻调制的该化合物相对应的纯组分，逐步分离每个〜2D峰。基于指数修正高斯（EMG）模型的仿真有助于此过程，其中EMG代表该化合物的所有〜2D峰的包络。峰参数可以通过了解源自同一主要化合物的纯已调制〜2D GC峰来限制，因为在低温调制系统的捕获和重新聚焦过程中，根据调制周期将其调制成几个部分。接下来，考虑该化合物的所有纯〜2D组分的相对面积，以重建主峰。这种利用第二维分离提供的附加信息的策略可以对GC×GC数据集进行有效的反卷积。非线性最小二乘曲线拟合（NLLSCF）允许恢复解析的〜2D色谱图。准确获取〜1D和〜2D中的纯轮廓有助于成分的定量和〜2D保留参数的预测，这对于定性和定量分析很重要。去卷积残差平方和与原始峰值响应之间的比率（R_（rr））被用作评估分辨率结果的有效指标。在这项工作中，将使用模拟和实验示例来开发和测试所提出的方法。这些研究的令人满意的性能通过最小和最大R_（rr）值1.34e-7％和1.09e-2％来验证; 1D和〜2D峰的去卷积分别为1.0e-3％和3.0e〜（-1）％。结果表明本技术适用于GC×GC数据处理。

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