激光诱导击穿光谱(LIBS)方法的优势之一就是可多元素同时检测。 为获得水体重金属LIBS多元素测量时的综合最佳信号输出, 本文利用BP神经网络拟合Pb, Cu和Ni三种元素特征谱线信背比(S/B)与延时门宽之间的数值关系, 同时采用DM设计实验数据作为校验样本, 保证BP神经网络模型的泛化能力。 基于上述数值模型利用遗传算法优化延时门宽两个测量参数, 定义了适应度函数, 得到延时门宽为(15.5和21.5 μs)时, 取最小值0.102 4, 此时三种元素综合信背比最大, 对比实验进一步验证了优化效果。 神经网络结合遗传算法的优化方法提高了水体重金属LIBS多元素测量时的综合信背比, 研究方法也为更多参数更多响应的实验系统优化提供了参考。

The one of the advantages about Laser Induced Breakdown Spectroscopy (LIBS) is multielement detection at the same time.In order to obtain the optimum signal in the multi-element measurements of water with LIBS, the present paper firstly models the numerical relationship between the signal-to-background ratio of characteristic spectral lines and the delay time and gate width time with BP neural network, using DM design experiment data as the checking sample to ensure the generalization ability of the BP neural network model. Based on the above model, genetic algorithm is used to optimize measurement parameters and the fitness function φ is defined. When the optimum delay time and gate width time is (15.5 μs, 21.5 μs), the minimum value of φ is 0.102 4. The optimization results of genetic algorithm are further confirmed with experimental results. So the method of parameters optimization overall improves S/B of multi-element measurements in water with LBS, and provides the reference for parameter optimization of other experiments.