激光诱导击穿光谱（LIBS）作为一种快速的化学组成分析技术，在冶金过程的原位、在线及远程分析方面展现了突出的应用前景和研究价值。利用神经网络建立定标模型，结合LIBS技术对不同品种钢中的Mn和Si组分进行定量分析，研究了不同输入方式对神经网络性能的影响，并与光谱分析中常用的内标法进行对比。结果表明，对于化学体系复杂的多基体钢的定量分析，神经网络定标法能够更充分利用光谱中的信息，有利于校正基体效应和谱线之间的干扰；但是，神经网络的输入方式对网络性能具有重要影响，只有在合理选择输入方式下才能有效提高测量重复性和准确性。

As a speedy analytical technique of chemical compositions, laserinduced breakdown spectroscopy (LIBS) is appealing in metallurgical industry for insitu, online or longrange applications. Combined with LIBS, neural networks are used to calibrate and quantify the concentration of Mn and Si of different kinds of steels. The performance of the neural networks with different inputs is studied. Compared with the common internal calibration methods, neural networks can utilize more information of spectra, and better correct the matrix effect and line interference. The inputs of the neural networks, however, need serious consideration, since they have a great effect on the measurement reproducibility and accuracy.