光谱处理技术对提升光丝诱导荧光光谱检测低浓度NaCl气溶胶能力的实验研究

Experimental Study of Spectral Processing Technology for Improving Detection Ability of Low-Concentration NaCl Aerosol by Filament-Induced Fluorescence Spectrum

大气污染对人类的生产生活有极大影响,气溶胶作为污染物的重要部分,不容忽视。提高对大气气溶胶浓度检测的精确性,尤其是低浓度气溶胶,具有十分重要的意义。本文基于光丝诱导荧光光谱技术,对NaCl气溶胶数据进行预处理,并结合偏最小二乘法建立预测模型,探索不同预处理方法对模型检测精度的影响。讨论如何科学合理地选择预处理方法,按照预处理方法效果分为散射校正、平滑去噪、基线校正3个方面,并提出波峰显著度算法。通过无预处理、单一预处理以及组合预处理进行最优预处理方法的选择,并分析其建模精度的影响。实验结果表明,应用多个预处理方法的组合,与无预处理相比,均方根误差降低至0.03,预测相对误差减少60%;与直接观察光谱信号选择预处理方法相比,根据光谱信噪比的提升及预测组分的建模效果可以更为准确地选择最佳预处理方法。该研究为开展低浓度大气污染物的分析研究提供了一定的参考。

Air pollution greatly impacts the productivity and life of people. Aerosol is an important pollutant and cannot be ignored. It is significant to improve the detection accuracy of atmospheric aerosol concentrations, especially lowconcentration aerosols. In this study, filament-induced fluorescence spectrum(FIFS) of NaCl aerosol is preprocessed and combined with partial least squares(PLSR) to establish a prediction model and explore the impact of different preprocessing methods on the detection accuracy of the model. To choose the preprocessing method, this study divides the preprocessing methods into three aspects according to their effects: scattering correction, smoothing and denoising, and baseline correction, and the significance of peak algorithm is proposed. The optimal preprocessing method is selected after comparing no preprocessing, single preprocessing, and combined preprocessing and analyzing the influence of different preprocessing methods on the accuracy of FIFS spectral prediction model. The experimental results show that the combined preprocessing of multiple methods reduces the root mean square error to 0. 03 compared with no preprocessing,and the relative prediction error is reduced by 60%. Compared with the direct observation of spectral signal selection preprocessing method, the best preprocessing method can be selected more accurately according to the improvement of spectral signal-to-noise ratio and the modeling effect of predicted components. The present study provides a reference for the analysis and research of low-concentration air pollutants.