Davis J M, Giddings J C
J Chromatogr. 1984 Apr 27;289:277-98. doi: 10.1016/s0021-9673(00)95095-7.
A statistical model of component-peak overlap in complex chromatograms is reviewed. Procedures for the estimation of the number of components in an analyte from its chromatograms by means of this model are restated. We note that the statistical model does not account for the effects of certain realistic chromatographic attributes. The influences of component-peak density, amplitude range, asymmetry, and noise levels on the estimation of the average component number are determined empirically with computer-generated chromatograms and are quantified by analyses of variance. We find that small departures from the model arise from variations in the magnitude of the amplitude range, density and the noise level. A large departure from theory arises from an application of the model to chromatograms containing highly asymmetric component-peaks. In spite of these departures, the estimation of the component number from chromatograms containing randomly distributed Gaussian component-peaks is uniformly more accurate with the use of the model than from a counting of peak maximum in chromatograms of extraordinarily high resolving power.
综述了复杂色谱图中组分峰重叠的统计模型。重申了利用该模型从分析物的色谱图估计组分数量的程序。我们注意到该统计模型未考虑某些实际色谱属性的影响。通过计算机生成的色谱图凭经验确定了组分峰密度、幅度范围、不对称性和噪声水平对平均组分数估计的影响，并通过方差分析进行了量化。我们发现，幅度范围、密度和噪声水平大小的变化会导致与模型出现小偏差。当该模型应用于包含高度不对称组分峰的色谱图时，会出现与理论的较大偏差。尽管存在这些偏差，但使用该模型从包含随机分布的高斯组分峰的色谱图中估计组分数，比从具有极高分辨率的色谱图中计数峰最大值要始终更准确。
