基于RBF网络的ICP-AES重叠谱线分离方法研究

Research on ICP-AES Overlapping Spectral Line Separation Method Based on RBF Network

在电感耦合等离子体原子发射光谱法(ICP-AES)分析中,光谱重叠干扰现象导致元素光谱测量结果产生误差。利用人工神经网络在非线性关系处理上的良好特性,可对重叠光谱拟合分峰问题有效处理,以达到测量误差校正的目的。在研究光谱叠加机理的基础上,建立高斯叠加模型,并综合应用正交试验思想构造神经网络数据样本;设计基于径向基函数(RBF)神经网络的谱峰分离参数预测模型,对分离谱线的峰值、峰位等参数进行预测,最终实现叠加谱线分析线与干扰线的分离。仿真结果表明,基于RBF网络的谱峰分离参数预测方法可应用于重叠光谱的多峰分离,其分峰效果良好,为光谱重叠干扰的校正打下基础。

In ICP-AES analysis,the phenomenon of spectral overlap interference causes the error of element spectral measurement results.The good characteristics of artificial neural network in nonlinear relation processing can help to solve the problem of overlapping spectrum fitting and peak dividing,so as to achieve the purpose of measurement error correction.In this paper,the mechanism of spectral superposition was studied,the Gaussian superposition model was established,and the neural network data samples were constructed by using the idea of orthogonal experiment.Based on the RBF neural network,a prediction model of spectral peak separation parameters was designed to predict the peak value,peak position and other parameters of the separated spectral lines,and finally the separation of the analysis line and interference line of the superimposed spectral lines was realized.The simulation results show that the prediction model based on RBF neural network can be applied to the multi peak separation of overlapped spectrum,and its peak separation effect is good,which lays a foundation for the correction of overlapped spectrum interference.