一种多源光谱融合的水样COD实验检测方法  

COD Detection Method of Water Quality Based on Multi-Source Spectral Fusion

作　　者：叶彬强 陈昶宏 曹雪杰 刘宏 汤斌 李东[1] 冯鹏[3] Ye Binqiang;Chen Changhong;Cao Xuejie;Liu Hong;Tang Bin;Li Dong;Feng Peng(School of Microelectronics and Communication Engineering,Chongqing University,Chongqing,400044,China;School of Artificial Intelligence,Chongqing University of Technology,Chongqing 400054,China;Key Laboratory of Optoelectronic Technology and Systems of Ministry of Education,Chongqing University,Chongqing 400044,China)

机构地区：[1]重庆大学微电子与通信工程学院,重庆400044 [2]重庆理工大学两江人工智能学院,重庆400054 [3]重庆大学光电技术及系统教育部重点实验室,重庆400044

出　　处：《光学学报》2024年第12期412-421,共10页Acta Optica Sinica

基　　金：国家自然科学基金(61805029);重庆市教委科学技术研究项目(KJQN202201110);重庆市九龙坡区基础研究与成果转化类科技计划(2022-02-003-Z);重庆市中小学创新人才培养工程项目(CY230903)。

摘　　要：提出了一种多源光谱融合的水质化学需氧量(COD)预测算法。该算法利用深度学习方法训练COD预测模型,并通过感知卷积网络确定紫外-可见吸收光谱和三维荧光光谱中各个位置的关注程度,不断移除关注程度较高的特征并重新训练网络,以发现可能被忽视的有效特征。进一步筛选并利用关注程度最高的融合特征位置,通过建立偏最小二乘(PLS)模型来预测COD,以更好地利用光谱数据中的所有有效特征。与支持向量回归(SVR)、PLS、间隔偏最小二乘(IPLS)模型相比,本文的留一法均方根误差(RMSE)分别减小了70.0%、75.1%和56.7%,十折交叉检验RMSE分别减小了64.3%、78.3%和64.6%。Objective Chemical oxygen demand(COD)refers to the quantity of reducing substances in water requiring oxidation.As the COD concentration becomes higher in water,the organic pollution is more severe.The decomposition of a large amount of organic pollutants excessively consumes dissolved oxygen in water,fostering anaerobic bacterium proliferation and resulting in water discoloration and malodor.Consequently,COD has become an important indicator for water pollution assessment.Spectral analysis for water quality COD assessment is one of the contemporary research focuses.Compared to conventional single-source spectral data prediction,using multi-source spectral data enables the extraction of richer feature information,thereby enhancing prediction accuracy.However,the key issue in detecting COD concentration using spectral methods is how to select appropriate feature wavelengths and establish regression models.Traditional feature extraction techniques(such as particle swarm optimization,ant colony optimization,and other swarm intelligence algorithms)exhibit screening efficacy.However,due to spectral data redundancy,more intelligent individuals are required for feature search,which greatly increases the computational load.If the number of intelligent individuals is reduced,the feature search range of spectral data needs to be narrowed,such as truncating the ultraviolet-visible spectrum to 200 to 400 nm and increasing the excitation and emission intervals of three-dimensional fluorescence spectroscopy.These methods will reduce the utilization range of spectral features.Therefore,we propose a multi-source spectral fusion algorithm for predicting COD concentration in water.The algorithm utilizes deep learning methods to train COD prediction models and determines the attention level of each position in the ultraviolet-visible absorption spectrum and three-dimensional fluorescence spectrum through a perceptual convolutional network.It continuously removes features with high attention levels and retrains the network to discov

关 键 词：化学需氧量 多源光谱融合 紫外-可见吸收光谱 三维荧光光谱 

分 类 号：X84[环境科学与工程—环境工程] O433.5[机械工程—光学工程]