GA-BP神经网络在检测微量磷酸盐中的应用

Application of GA-BP Neural Network in Detection of Trace Phosphate

基于罗丹明6G的分子荧光原理,通过对比不同实验条件下得到的罗丹明6G荧光光谱,得出p H为1条件下的相对荧光强度最大。罗丹明6G试剂中加入钼酸铵、磷酸二氢钾、硫酸试剂生成络合物后,罗丹明6G的相对荧光强度值有所下降,在一定范围内表现出线性关系,罗丹明6G荧光峰的位置没有发生变化。基于遗传算法-逆向误差传播（GA-BP）神经网络构建了输入节点数为36×18的矩阵、输出节点数为1×18的矩阵、以检测磷酸盐浓度为目的的非线性模型。网络训练中,误差精度为10^-3,输出与期望的相关系数为0.998,网络预测中,平均回收率为99%,平均标准偏差值为1.79%,达到了理想的检测效果。证明此网络适用于检测0~2.00 mg/L的磷酸盐溶液。提供了一种快速、有效检测磷酸盐浓度的方法,有助于环境检测技术的发展和应用。

Based on the principle of molecule fluorescence of rhodamine 6G, the fluorescence spectra under different experimental conditions are compared and the maximum fluorescence intensity is obtained when pH is 1. When molybdate, potassium dihydrogen phosphate and sulfuric acid are added into the rhodamine 6G reagent the complex is generated and the fluorescence intensity of rhodamine 6G declines. Within a certain range, it exhibits linear relationship. The position of fluorescence peak does not change. A nonlinear model is constructed based on genetic algorithm-back propagation （GA-BP） neural network which consists of a 36×18 matrix as inputs and a 1×18 matrix as outputs, and its purpose is to detect the phosphate concentration. In network training, the error accuracy is 10^-3 and the correlation coefficient between the outputs and the expectations is 0.998. In network prediction, the average recovery is 99%, while the average standard deviation is 1.79%, reaching the ideal results. Therefore, this network can better detect phosphate concentration of 0- 2.00 mg/L. In summary, a quick and effective way to detect phosphate concentration is provided, which helps promote the development and application of environmental monitoring technique.