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基于紫外-荧光多光谱融合的水质化学需氧量检测

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

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摘要

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。

Abstract

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.

Newport宣传-MKS新实验室计划
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中图分类号:O433.4;O657.3

DOI:10.3788/lop55.113003

所属栏目:光谱学

基金项目:国家重点研发计划项目(2017YFC1403800)、河北省重点研发计划项目(18273302D)、内蒙古民族大学博士科研启动基金(BS432)、内蒙古民族大学科研项目(NMDYB17162)

收稿日期:2018-04-24

修改稿日期:2018-05-28

网络出版日期:2018-06-07

作者单位    点击查看

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

联系人作者:毕卫红(bwhong@ysu.edu.cn)

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引用该论文

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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