Department of Chemistry, Box 351700, University of Washington, Seattle, WA 98195, USA.
Department of Chemistry, Box 351700, University of Washington, Seattle, WA 98195, USA.
J Chromatogr A. 2019 Sep 13;1601:319-326. doi: 10.1016/j.chroma.2019.05.005. Epub 2019 May 4.
Evaluation of a recently developed data reduction method for gas chromatography time-of-flight mass spectrometry (GC-TOFMS) is presented in the context of the statistical model of overlap (SMO) using simulated chromatographic data. The two-dimensional mass cluster plot method (2D m/z cluster plot method) significantly improves separation visualization by measuring the retention time, t, and peak width-at-base, w, of each analyte peak on a per mass channel, m/z, basis and plotting w versus t as a single point for each peak. Additional selectivity is provided by the peak width dimension, allowing for the differentiation of "pure" or selective m/z and shared or overlapped m/z. Analyte clusters in the 2D mass cluster plot are defined based on clustering of individual points, representing the selective m/z for those analytes, and encompassed by a box of user-specified size. The method is applied to simulated chromatographic data with a random, independent distribution of analyte peaks and constant peak w. Two levels of chromatographic saturation factor, α, and two sets of analyte mass spectra with varying spectral similarity are studied to assess method performance. The percentage of analyte clusters found relative to the number of analytes simulated in the chromatogram increases as the box size (analogous to chromatographic resolution, R) is decreased, resulting in an R limit of 0.05 for the method. Additionally, the percentage of analyte clusters discovered also increases with lower α and greater dissimilarity between analyte mass spectra, demonstrating the immense benefit of improving the chromatographic separation and chemical selectivity in analyte discovery, identification, and quantification.
本文介绍了一种新开发的数据缩减方法在统计学模型重叠（SMO）的应用，该方法应用于气相色谱飞行时间质谱（GC-TOFMS）数据。二维质量聚类图方法（2D m/z 聚类图方法）通过测量每个质量通道（m/z）上每个分析物峰的保留时间（t）和峰底宽（w），并将 w 相对于 t 绘制为每个峰的单个点，从而显著提高了分离可视化效果。通过峰宽维度提供了额外的选择性，允许区分“纯”或选择性 m/z 和共享或重叠的 m/z。2D 质量聚类图中的分析物聚类基于代表这些分析物选择性 m/z 的单个点的聚类来定义，并由用户指定大小的框包围。该方法应用于具有随机、独立分布的分析物峰和恒定峰宽的模拟色谱数据。研究了两种色谱饱和度因子（α）水平和两组具有不同光谱相似性的分析物质谱，以评估方法性能。与在色谱图中模拟的分析物数量相比，发现的分析物聚类的百分比随着框大小（类似于色谱分辨率，R）的减小而增加，导致该方法的 R 限制为 0.05。此外，随着 α 的降低和分析物质谱之间的差异增大，发现的分析物聚类的百分比也增加，这表明在分析物发现、鉴定和定量中提高色谱分离和化学选择性具有巨大的益处。
