基于Mie散射的水体紫外-可见光谱浊度干扰补偿

发布时间：2018-02-23 22:35

本文关键词： 光谱学紫外-可见光谱浊度补偿 Mie散射光全散射法水质检测　出处：《光学学报》2017年02期 　论文类型：期刊论文

【摘要】：紫外-可见光谱法水质检测中,检测设备面对多样性的地表水体容易受到悬浮颗粒物引起的浊度干扰,导致谱线整体非线性抬升,测量精度显著下降。研究了一种基于Mie散射理论的紫外-可见水质光谱浊度干扰补偿算法,借以提高可溶有机物含量的检测精度。通过截取水样本紫外-可见光谱中主要由颗粒物散射引起的450~1100nm段光谱,采用基于Mie散射理论的光全散射颗粒物粒径分析法,重建了水样本所含颗粒物的粒径分布。利用粒径分布的二次反演,估计了220~450nm可溶有机污染特征光谱段中由颗粒物引起的消光值,实现了对浊度干扰的精确补偿。实验结果表明,该算法对不同地表水样本均表现出了良好的浊度补偿效果,化学需氧量解算精度得到较大提高。且补偿算法无需大量实验获取先验数据,可提高紫外-可见光谱法水体可溶有机物检测的准确性和检测部署的灵活性与适应性,具备实用价值。
[Abstract]:In the detection of water quality by UV-Vis spectroscopy, the detection equipment is vulnerable to the turbidity interference caused by suspended particulates in the face of the diversity of surface water bodies, which leads to the overall nonlinear uplift of the spectral lines. The accuracy of measurement is obviously decreased. Based on the theory of Mie scattering, a compensation algorithm for the turbidity interference of UV-Vis water quality spectrum is studied. In order to improve the detection accuracy of soluble organic matter content, the particle size analysis method based on Mie scattering theory was used to analyze the UV visible spectrum of water samples, which was mainly caused by the scattering of particles at 450 ~ 1100nm. The particle size distribution of the water sample was reconstructed. By using the quadratic inversion of the particle size distribution, the extinction value caused by the particles in the characteristic spectrum of 220 ~ 450nm soluble organic pollution was estimated, and the accurate compensation for the turbidity interference was realized. The algorithm has a good turbidity compensation effect on different surface water samples, and the precision of calculating chemical oxygen demand is greatly improved, and the compensation algorithm does not need a large number of experiments to obtain prior data. It can improve the accuracy, flexibility and adaptability of detection of soluble organic matter in water body by UV-Vis spectrometric method, and has practical value.
【作者单位】：重庆大学光电技术及系统教育部重点实验室;重庆工业职业技术学院自动化学院;
【基金】：国家自然科学基金(61401049) 四川省科技支撑计划(2012SZ0111) 重庆市研究生科研创新项目(CYS14039)
【分类号】：X832;O657.3

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