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Detection of Chemical Oxygen Demand in Water Based on Multi-Spectral Fusion of Ultraviolet and Fluorescence

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提出了一种基于紫外和荧光多光谱融合的水质化学需氧量(COD)的检测方法。实验样本为包含近岸海水和地表水在内的53份水样,采用标准化学方法获取样本的COD理化值,利用紫外-可见光谱仪和荧光分光光度计采集样品的紫外吸收光谱和三维荧光光谱,对光谱数据进行处理后建模。采用蚁群-区间偏最小二乘法(ACO-iPLS)作为特征提取算法,采用粒子群优化的最小二乘支持向量机(PSO-LSSVM)算法作为建模方法,分别建立基于紫外吸收光谱数据和单激发波长下的荧光发射光谱数据的预测模型,以及紫外-荧光多光谱数据级融合模型和特征级融合模型,并对各类模型的预测效果进行对比。结果表明:基于紫外-荧光多光谱特征级融合模型的预测效果最优,该模型预测水质COD的精度更高,其校正集决定系数为0.9999,检验集的预测决定系数为0.9912,外部检验均方根误差为1.1297 mg/L。本研究为水质COD的快速检测提供了一种新的研究思路和解决方法。


A method for detecting chemical oxygen demand (COD) in water based on ultraviolet and fluorescence multi-spectral fusion is proposed. The experimental samples are 53 actual water samples, including coastal seawater and surface water. The physicochemical values of the experimental samples are calculated by the standard chemical method, and the ultraviolet absorption spectra of the samples are collected by the ultraviolet-visible spectrometer, and the three-dimensional fluorescence spectra are collected by fluorescence spectrophotometer, then the processed spectral data are used to build model. Using the ant colony-interval partial least squares(ACO-iPLS) as feature extraction algorithm and the particle swarm optimization least squares support vector machine(PSO-LSSVM) as modeling method, we establish the prediction model based on ultraviolet absorption spectra and fluorescence emission spectra at single excitation wavelength, the data level fusion model and the feature level fusion (mid-level data fusion, MLDF) model based on ultraviolet and fluorescence multi-spectral information, respectively. And the prediction results of various models are compared. The results show that the prediction effect of the MLDF model based on ultraviolet and fluorescence multi-spectral information is optimal, and the prediction accuracy of COD in water is relatively high. The determination coefficient of calibration set is 0.9999, the prediction determination coefficient of validation set is 0.9912, and the root mean square error in prediction set is 1.1297 mg/L. It provides a new research idea and solution for the rapid detection of COD in water.









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周昆鹏:内蒙古民族大学物理与电子信息学院, 内蒙古 通辽 028000
白旭芳:内蒙古民族大学物理与电子信息学院, 内蒙古 通辽 028000
毕卫红:燕山大学信息科学与工程学院, 河北省特种光纤与光纤传感重点实验室, 河北 秦皇岛 066004


【1】Gutiérrez-Capitán M, Baldi A, Gómez R, et al. Electrochemical nanocomposite-derived sensor for the analysis of chemical oxygen demand in urban wastewaters[J]. Analytical Chemistry, 2015, 87(4): 2152-2160.

【2】Shen B J, Zhao Y Y, Xu Y, et al. Determination of low chemical oxygen demand of waste water with high chloride by fast digestion-spectrophotometric method[J]. Chemical Analysis and Meterage, 2016, 25(3): 69-72.
沈碧君, 赵洋甬, 徐运, 等. 快速消解分光光度法测定高氯废水中低浓度化学需氧量[J]. 化学分析计量, 2016, 25(3): 69-72.

【3】Gimeno O, García-Araya J F, Beltrán F J, et al. Removal of emerging contaminants from a primary effluent of municipal wastewater by means of sequential biological degradation-solar photocatalytic oxidation processes[J]. Chemical Engineering Journal, 2016, 290: 12-20.

【4】Jin B H, He Y, Shen J C, et al. Measurement of chemical oxygen demand (COD) in natural water samples by flow injection ozonation chemiluminescence (FI-CL) technique[J]. Journal of Environmental Monitoring, 2004, 6(8): 673-678.

【5】Hou D B, Zhang J, Chen L, et al. Water quality analysis by UV-vis spectroscopy: a review of methodology and application[J]. Spectroscopy and Spectral Analysis, 2013, 33(7): 1839-1844.
侯迪波, 张坚, 陈泠, 等. 基于紫外-可见光光谱的水质分析方法研究进展与应用[J]. 光谱学与光谱分析, 2013, 33(7): 1839-1844.

【6】Zhu Y N, Yang P, Yang X Y, et al. Classification of fresh meat species using laser-induced breakdown spectroscopy with support vector machine and principal component analysis[J]. Chinese Journal of Analytical Chemistry, 2017, 45(3): 336-341.
朱毅宁, 杨平, 杨新艳, 等. 支持向量机结合主成分分析辅助激光诱导击穿光谱技术识别鲜肉品种[J]. 分析化学, 2017, 45(3): 336-341.

【7】Yuan J Z, Lu Q P, Wu C Y, et al. Noninvasive human triglyceride detecting with near-infrared spectroscopy[J]. Spectroscopy and Spectral Analysis, 2018, 38(1): 42-48.
袁境泽, 卢启鹏, 吴春阳, 等. 近红外光谱人体血液甘油三酯无创检测[J]. 光谱学与光谱分析, 2018, 38(1): 42-48.

【8】Kumar A, Jain S K. Development and validation of UV-spectroscopy based stability indicating method for the determination of fluoxetine hydrochloride[J]. Analytical Chemistry Letters, 2016, 6(6): 894-902.

【9】Liu R X, Chen L L, Zhang H Y, et al. A label-free single photonic quantum well biosensor based on porous silicon for DNA detection[J]. Optoelectronics Letters, 2013, 9(3): 225-228.

【10】Dong M, Sui Y, Li G L, et al. Mid-infrared carbon monoxide detection system using differential absorption spectroscopy technique[J]. Optoelectronics Letters, 2015, 11(6): 469-472.

【11】Wu D C, Wei B, Tang G, et al. Turbidity disturbance compensation for UV-VIS spectrum of waterbody based on Mie scattering[J]. Acta Optica Sinica, 2017, 37(2): 0230007.
吴德操, 魏彪, 汤戈, 等. 基于Mie散射的水体紫外-可见光谱浊度干扰补偿[J]. 光学学报, 2017, 37(2): 0230007.

【12】Dahlbacka J, Nystrm J, Mossing T, et al. On-line measurement of the chemical oxygen demand in wastewater in a pulp and paper mill using near infrared spectroscopy[J]. Spectral Analysis Review, 2014, 2(4): 19-25.

【13】Chen M F, Wu J, Lü Y L, et al. Fluorescence properties of municipal wastewater[J]. Acta Optica Sinica, 2008, 28(3): 578-582.
陈茂福, 吴静, 律严励, 等. 城市污水的三维荧光指纹特征[J]. 光学学报, 2008, 28(3): 578-582.

【14】Wang J, Zhang F, Wang X P, et al. Three-dimensional fluorescence characteristics by parallel factor method coupled with self-organizing map and its relationship with water quality[J]. Acta Optica Sinica, 2017, 37(7): 0730003.
王娟, 张飞, 王小平, 等. 平行因子法结合自组织映射神经网络的三维荧光特征及其与水质的关系[J]. 光学学报, 2017, 37(7): 0730003.

【15】Qian C, Wang L F, Chen W, et al. Fluorescence approach for the determination of fluorescent dissolved organic matter[J]. Analytical Chemistry, 2017, 89(7): 4264-4271.

【16】Yang L Y, Hur J, Zhuang W N. Occurrence and behaviors of fluorescence EEM-PARAFAC components in drinking water and wastewater treatment systems and their applications: a review[J]. Environmental Science and Pollution Research, 2015, 22(9): 6500-6510.

【17】Wu G Q, Bi W H. Research on chemical oxygen demand optical detection method based on the combination of multi-source spectral characteristics[J]. Spectroscopy and Spectral Analysis, 2014, 34(11): 3071-3074.
吴国庆, 毕卫红. 多源光谱特征组合的COD光学检测方法研究[J]. 光谱学与光谱分析, 2014, 34(11): 3071-3074.

【18】Cao H. Research on rapid determination of organic matter concentration in aquaculture water using multi-source spectral data fusion[D]. Hangzhou: Zhejiang University, 2014: 58-68.
曹泓. 基于多源光谱数据融合的水产养殖水质有机物浓度快速检测研究[D]. 杭州: 浙江大学, 2014: 58-68.

【19】Zepp R G, Sheldon W M, Moran M A. Dissolved organic fluorophores in southeastern US coastal waters: correction method for eliminating Rayleigh and Raman scattering peaks inexcitation-emission matrices[J]. Marine Chemistry, 2004, 89: 15-36.


Zhou Kunpeng,Bai Xufang,Bi Weihong. Detection of Chemical Oxygen Demand in Water Based on Multi-Spectral Fusion of Ultraviolet and Fluorescence[J]. Laser & Optoelectronics Progress, 2018, 55(11): 113003

周昆鹏,白旭芳,毕卫红. 基于紫外-荧光多光谱融合的水质化学需氧量检测[J]. 激光与光电子学进展, 2018, 55(11): 113003

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