应用荧光光谱和径向基函数神经网络定量检测三聚氰胺  

Quantitative Determination of Melamine by Fluorescence Spectroscopy and Radial Basis Function Neural Networks

作　　者：陈国庆[1] 魏柏林[1] 王俊[1] 吴亚敏[1] 高淑梅[1] 孔艳[1] 朱拓[1] 

机构地区：[1]江南大学理学院,江苏无锡214122

出　　处：《光谱学与光谱分析》2010年第1期239-242,共4页Spectroscopy and Spectral Analysis

基　　金：江苏省项目(JH08-18;CX08B-088Z);教育部项目(200802950005);国家"863"项目(2007AA10Z353)资助

摘　　要：实验发现三聚氰胺溶液在紫外光激发下产生较强荧光,测得其荧光峰在310～600 nm之间,荧光峰值波长为420 nm左右,荧光相对强度与三聚氰胺溶液浓度呈现复杂的非线性关系。提出了采用径向基函数神经网络结合荧光光谱对三聚氰胺溶液浓度进行测定的方法。对每个样本选取30个发射波长值所对应的荧光强度作为网络数据,训练、建立了径向基函数神经网络。应用训练好的径向基函数神经网络,对5种三聚氰胺溶液的浓度进行预测,结果相对误差分别为0.93%,0.09%,0.31%,1.55%,4.61%。该方法能快捷、准确地测定三聚氰胺在溶液中的含量,为三聚氰胺检测及食品安全监管提供了一种新方法。Based on the experimental study, it was found that melamine solution excited by UV light can generate a strong fluorescence. The fluorescence spectrum is within a range from 310 to 600 nm, the peak wavelength of the fluorescence is about 420 nm, and the relationship between fluorescence intensity and melamine solution concentration is nonlinear. A method for the determination of melamine solution concentration was presented, which was based on fluorescence spectroscopy and radial basis function neural networks. For each sample, 30 emission wavelength values were selected, the fluorescence intensity corresponding to the selected wavelength was used as the network data, and a radial basis function neural network was trained and constructed. The trained radial basis function neural network was employed to predict the melamine solution concentration in five kinds of samples, and the relative errors of the results were 0.93%, 0.09%, 0.31%, 1. 55% and 4. 61%, respectively. The results show that this method can determine the content of melamine quickly and accurately. The whole research outcomes will provide a new method for determining the content of melamine and food safety supervision.

关 键 词：三聚氰胺 荧光光谱 径向基函数神经网络 定量预测 食品安全 

分 类 号：O657.3[理学—分析化学]