中文摘要：

文章结合两种化学计量学技术，研制了一种小波包变换广义回归神经网络（WFFGRNN）方法，对光谱严重重叠的三种有机化合物进行同时测定。该法结合小波包变换（WFF）和广义回归神经网络（GRNN）改进了除噪质量和预测能力。通过最佳化，选择了小波函数、小波包分解水平及GRNN的平滑因子。偏最小二乘（PLS）法用于比较研究，编制了三个程序（PWPTGRNN，PGRNN和PPLS）进行相关计算。结果表明，WFFGRNN法是成功的且优于GRNN及PLS方法，与GRNN方法比较所有组分质量浓度的预测值与实际值的相对预测标准误差由4．0％降低为2．3%。

英文摘要：

A wavelet packet transform-based generalized regression neural network （WPTGRNN） was developed to perform sim ultaneous spectrophotometrie determination of p-nitroaniline, ccnaphthylamine and benzidine. This method combines wavelet packet transform （WPT） with generalized regression neural network （GRNN） for improving the quality of noise removal and enhancing the ability of prediction. Wavelet packet representations of signals provided a local time-frequency description and separation ability between information and noise. The quality of noise removal can be further improved by using best-basis algorithm and thresholding operation. Generalized regression neural network （GRNN） was applied for overcoming the convergence problem encountered in hack propagation training and facilitating nonlinear calculation. The GRNN is also advantageous in that the training process is much faster and without making any assumption about the form of the prediction model. By optimization, the wavelet function, decomposition level and smoothing factor of GRNN were selected. The partial least squares （PLS） method was used for comparative study. PLS method uses both the response and concentration information to enhance its ability of predic tion. Three programs, PWPTGRNN, PGRNN and PPLS, were designed to perform relative calculations. Experimental results showed WPTGRNN method to be successful and better than others. Compared with GRNN method, the relative standard errors of all components between the actual and estimated values of mass concentration for WPTGRNN method decreased from 4.0% to 2.3%. Aniline- type compounds are widely applied in industries such as chemistry, printing and pharmacy, and are one of the most important raw materials for synthetic medicine, dye, insecticides, polymer and explosives. Aniline-type compounds are highly poisonous, and can also cause cancer. Simultaneous determinations of aniline-type compounds are very important in environmental and industrial analysis.