针对超短脉冲激光与气体相互作用产生的非线性荧光光谱的特点, 提出了光谱分析的预处理、特征提取以及定量分析的有效方法。采用小波算法对光谱数据进行压缩和降噪处理, 使光谱数据由3979点压缩降噪至664点。对处理后的光谱特征峰团的强度进行主成分分析, 结果表明2个主成分即可包含98%的光谱信息。采用第一主成分对不同浓度下的谱线强度进行拟合与误差计算, 有效地提取了与气体浓度有关的特征参量, 实现了气体的定量分析。同已有的定量分析方法相比, 定量分析的精度有较大提高, 特别在所关心的低浓度情况下, 定量分析的精度改善更为明显。3种气体的拟合与计算显示同以往方法相比误差由0.2694降为0.02。

A pre-process, feature extraction and quantitative analysis approach based on principal component analysis (PCA) is proposed to analyze the complicated nonlinear fluorescent spectra, emitted by the interaction between femto-second(fs) laser and the impurities in air. The spectral data is denoised and compressed from 3979 to 664 points using wavelet transform (WT). By fitting the feature peaks of the compressed spectra with different concentration impurities using PCA, the monotone relation between intensity and concentration is identified and it can be used to perform quantitative analysis. Simulation results on three kinds of impurities with low concentration show that the first two principal components can cover 98% information, and the quantitative analysis method based on the first principal component can effectively reduce the error from 0.2694 to 0.02 compared with previous method.