色谱重叠峰分解的神经网络法

对于色谱图中两个相互重叠的峰, 本文提出了一种分解方法。首先在重叠峰的一阶导数曲线上取出五个无因次特征值, 然后用多层前传网来表达这五个特征值和重叠峰中子峰面积分率之间的关系。一系列实验的结果表明, 用神经网络方法所得子峰面积的准确度, 优于传统的垂线法及函数拟合法, 而且计算工作量较小, 可用于实时处理。

Resolution of chromatography overlapping peaks by artificial neural network

A novel method for resoluting chromatographic overlapping peaks is proposed in this paper. The basic idea of this method is to find out a set of parameters which characterize the shape of overlapping peaks, and a multi-layered non-linear feed forward network for quantitatively correlating the parameters with the area percentage of individual peak is used. The proposed method performs very well with high accuracy and less computing time in comparing with the conventional methods.