基于径向基函数神经网络的荧光光谱技术在菌种识别中的应用

对嗜酸乳杆菌、变异链球菌和保加利亚乳杆菌这三种菌的荧光光谱进行研究，发现在紫外光的激励下，益生菌溶液发出荧光.在最佳激发波长290　nm的激励下，荧光峰值在300—650　nm范围内.采用小波变换对测得的150组光谱数据进行压缩，压缩后每组数据由原来的1341个点减少为168个点，既保留了原图谱的特征，又提高了神经网络的处理速度.径向基函数神经网络方法对压缩后的数据进行研究，对每种菌的40组实验数据进行训练，在此基础上对30组未知数据进行识别.结果表明经过训练之后，径向基函数神经网络能够准确预测未知菌种. 关键词： 荧光光谱 径向基函数神经网络 数据压缩

Application of fluorescence spectra based on radial basis function neural network in identification of bacteria

The results of research on fluorescence spectra of three kinds of probiotic bacteria(lactobacillus acidophilus,streptococcus mutans and lactobacillus bulgaricus)show that the bacteria emit fluorescence when irradiated by ultraviolet light.The spectra are in the range of 300-650 nm with the excitation wavelength of 290 nm.Wavelet transform is used to compress the 150 groups of spectra data,and the number of data points in each group is reduced from 1341 to 168,which not only keeps the character of original spectra unchanged,but also improves the processing speed of neural network.Radial basis function neural network is applied for processing the compressed data.40 groups of data of each strain are used to be trained,and the other 30 groups of data of which the kinds of bacteria are not given are used for prediction.The result shows that radial basis function neural network can identify the unknown bacterial strain accurately.