Abstract：
Analytical data processing often requires the comparison of data, i.e. finding similarities and differences within separations. In this context, a peak-tracking algorithm was developed to compare multiple datasets in one-dimensional (1D) and two-dimensional (2D) chromatography. Two application strategies were investigated: i) data processing where all chromatograms are produced in one sequence and processed simultaneously, and ii) method optimization where chromatograms are produced and processed cumulatively. The first strategy was tested on data from comprehensive 2D liquid chromatography and comprehensive 2D gas chromatography separations of academic and industrial samples of varying compound classes (monoclonal-antibody digest, wine volatiles, polymer granulate headspace, and mayonnaise). Peaks were tracked in up to 29 chromatograms at once, but this could be upscaled when necessary. However, the peak-tracking algorithm performed less accurate for trace analytes, since, peaks that are difficult to detect are also difficult to track. The second strategy was tested with 1D liquid chromatography separations, that were optimized using automated method-development. The strategy for method optimization was quicker to detect peaks that were still poorly separated in earlier chromatograms compared to assigning a target chromatogram, to which all other chromatograms are compared. Rendering it a useful tool for automated method optimization.
摘要：
分析数据处理通常需要对数据进行比较，即在分离中找到异同。在这种情况下，开发了一种峰值跟踪算法来比较一维（1D）和二维（2D）色谱中的多个数据集。研究了两种应用策略：i）数据处理，其中所有色谱图均以一个序列生成并同时处理，和ii)方法优化,其中累积地产生和处理色谱图。第一种策略是对来自不同化合物类别的学术和工业样品（单克隆抗体消化，葡萄酒挥发物,聚合物颗粒顶部空间，和蛋黄酱）。一次在多达29个色谱图中跟踪峰值，但必要时可以扩大规模。然而,峰值跟踪算法对痕量分析物的准确性较低，因为,难以检测的峰值也难以追踪。第二种策略用一维液相色谱分离进行了测试，使用自动化方法开发进行了优化。与分配目标色谱图相比，方法优化策略可以更快地检测早期色谱图中分离不良的峰。与所有其他色谱图进行比较。渲染它是自动方法优化的有用工具。
