提出了一种基于遗传神经网络定量分析模型的激光诱导击穿光谱（LIBS）分析技术。采用误差反向传播（BP）算法构造三层神经网络（ANN）结构，通过遗传算法对神经网络权值和阈值进行优化，并将该定量分析模型与LIBS技术有机结合，实现了元素含量的高精度检测。对土壤中的Ba和Ni元素进行定量检测，平均相对误差分别为4.15%和6.06%，相关系数分别为0.983和0.990，检测精度明显优于BP-ANN方法和光谱分析中常用的内标法。研究表明遗传神经网络建模方法具有很好的预测效果，为LIBS技术进行元素高精度检测提供了一种新的建模方法。

A quantitative analysis technique based on laser induced breakdown spectroscopy (LIBS) of neuro-genetic model is proposed. A three-layer back-propagation (BP) artificial neural networks (ANN) is constructed as a basic calibration model for LIBS analysis. The weight and threshold of the ANN are optimized by genetic algorithm. By combining calibration model with LIBS technique, high precision detection is achieved. The concentrations of Ba and Ni in soil samples are detected by using the given quantitative analysis technique. The mean relative errors are 4.15% and 6.06% respectively, and the correlation coefficients are 0.983 and 0.990 respectively. The presented results demonstrate that the neuro-genetic approach performs better than BP-ANN and conventional calibration method in LIBS quantitative analysis. The analytical results based on neuro-genetic approach in this study are well predicted, which provide a new modeling of high accuracy quantitative elemental analysis for LIBS technique.