Automatic program for peak detection and deconvolution of multi-overlapped chromatographic signals part II: peak model and deconvolution algorithms.
Automatic program for peak detection and deconvolution of multi-overlapped chromatographic signals part II: peak model and deconvolution algorithms.
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DOI：
10.1016/j.chroma.2005.03.072
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发表时间：
2005-11
期刊：
影响因子：
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通讯作者：
G. Vivó-Truyols;J. R. Torres-Lapasió;A. V. Nederkassel;Y. Heyden;D. Massart
中科院分区：
文献类型：
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作者：
G. Vivó-Truyols;J. R. Torres-Lapasió;A. V. Nederkassel;Y. Heyden;D. Massart
提出了几种非线性回归峰值反褶积的关联算法。这些程序与第一部分中概述的峰值检测方法一起，允许实现能够处理多个重叠信号的自动方法，几乎不需要用户交互。基于色谱信号的多元选择性评价的标准用于自动选择每种色谱情况下最有效的去卷积程序。以这种方式，在高重叠的情况下避免了非最优局部解，并且在高分辨率的情况下获得了短的计算时间。一种新的算法，拟合原始信号和二阶导数证明，以避免局部最优的中间共洗脱情况。这允许实现全局最优，而不需要用户的背景知识。一个先前报道的峰值模型，一个高斯与多项式的标准偏差，其复杂性可以调制，以提高拟合质量，被应用。然而，对原始配方进行了修改，以考虑峰区域以外的基线。此外，通过误差传播理论自动选择最佳模型复杂度。该方法能够同时处理多个相关的色谱图。该软件进行了测试与整体硅胶柱获得的模拟和实验色谱图。
Several interlinked algorithms for peak deconvolution by non-linear regression are presented. These procedures, together with the peak detection methods outlined in Part I, have allowed the implementation of an automatic method able to process multi-overlapped signals, requiring little user interaction. A criterion based on the evaluation of the multivariate selectivity of the chromatographic signal is used to auto-select the most efficient deconvolution procedure for each chromatographic situation. In this way, non-optimal local solutions are avoided in cases of high overlap, and short computation times are obtained in situations of high resolution. A new algorithm, fitting both the original signal and the second derivatives is proved to avoid local optima in intermediate coelution situations. This allows achieving the global optimum without the need of background knowledge by the user. A previously reported peak model, a Gaussian with a polynomial standard deviation whose complexity can be modulated to enhance the fitting quality, was applied. However, the original formulation was modified to account baseline outside the peak region. Also, the optimal model complexity was auto-selected via error propagation theory. The method is able to process simultaneously several related chromatograms. The software was tested with both simulated and experimental chromatograms obtained with monolithic silica columns.