为了对水中的有机污染物进行绿色、 快速、 准确的检测, 提出了一种基于荧光多光谱融合的水质化学需氧量(Chemical Oxygen Demand, COD)的检测方法。 实验样本为包含近岸海水和地表水在内的实际水样53份, 采用标准化学方法获取样本的化学需氧量的理化值, 利用荧光分光光度计采集样本的三维荧光光谱并对光谱数据进行处理和建模。 在200~300 nm(间隔5 nm)的激发波长范围内将三维光谱展开成二维的发射光谱(发射波长范围250~500 nm, 间隔2 nm)。 采用ACO-iPLS(蚁群-区间偏最小二乘)算法提取发射光谱特征, PSO-LSSVM(粒子群优化的最小二乘支持向量机)算法建立预测模型, 分别建立了单激发波长下的荧光发射光谱数据预测模型、 多激发波长下发射光谱的数据级融合(LLDF)预测模型以及多激发波长下发射光谱的特征级融合(MLDF)预测模型, 通过对预测效果的对比, 得出结论。 实验结果表明, 对于不同激发波长下荧光发射光谱数据而言, 265 nm激发光作用下的发射谱数据的预测模型最优, 其检验集决定系数RP2和外部检验均方根误差RMSEP分别为0.990 1和1.198 6 mg·L^-1; 对于荧光多光谱数据级融合模型(简写为: LLDF-PSO-LSSVM)而言, 在235, 265和290 nm激发光作用下的发射光谱的LLDF模型效果最优, 其检验集的Rp2和RMSEP分别为0.992 2和1.055 1 mg·L^-1; 对于荧光多光谱特征级融合模型(MLDF-PSO-LSSVM)而言, 在265, 290和305 nm激发光作用下的荧光发射光谱的MLDF模型效果最优, 其Rp2=0.998 2, RMSEP=0.534 2 mg·L^-1。 综合比较各类建模结果可知, MLDF-PSO-LSSVM的模型效果最优, 说明基于荧光发射光谱数据, 采用多光谱特征级融合模型检测水质COD时, 检测的精度更高, 预测效果更好。

To conduct green, rapid and accurate detection of organic pollutants in water, the current paper proposes a detection method of Chemical Oxygen Demand (COD) based on fluorescence multi-spectral fusion. The experimental samples consist of 53 actual water samples including inshore seawater and surface water. The physicochemical valuesof COD of the samples are obtained by standard chemical methods. A fluorescence spectrophotometer is used to collect the three-dimensional fluorescence spectra of the samples, and the spectral data are processed and modeled. The three-dimensional fluorescence spectrum is spread at the excitation wavelength in the excitation wavelength range of 200~330 nm and the emission wavelength range of 250~500 nm, with excitation wavelength interval being 5 nm, and the emission wavelength interval 2 nm. With the ant colony optimization-interval partial least squares (ACO-iPLS) as the feature extraction algorithm and the least squares support vector machine algorithm optimized by particle swarm optimization (PSO-LSSVM) as the modeling method, the prediction model of fluorescence emission spectral data at a single excitation wavelength, the fluorescence multi-spectral data-level fusion (Low-Level Data Fusion, LLDF) model and the fluorescence multi-spectral feature-level fusion (Mid-Level Data Fusion, MLDF) model are built respectively, and the prediction effects of various models are compared. The results show that there exist some differences in the prediction effect of the models for the fluorescence emission spectrum data at different excitation wavelengths. The prediction model of the fluorescence emission spectrum data at the excitation wavelength of 265 nm is optimal, with determinant coefficient (Rp2) and the root mean square error in prediction (RMSEP) of the calibration set being 0.990 1 and 1.198 6 mg·L^-1 respectively. For fluorescence multi-spectral data-level fusion models, fluorescence emission spectra at excitation wavelengths of 235, 265, and 290 nm (abbreviated as: LLDF-PSO-LSSVM) have the best prediction effect, with the results ofRp2 and RMSEP being 0.992 2 and 1.055 1 mg·L^-1 respectively. For fluorescence multi-spectral feature-level fusion models, fluorescence emission spectra at excitation wavelengths of 265, 290, and 305 nm (abbreviated as: MLDF-PSO-LSSVM) have the best prediction effect, with theRp2being 0.998 2 and the RMSEP being 0.534 2 mg·L^-1. A comprehensive comparison of various modeling results shows that the model of MLDF-PSO-LSSVM has the best performance, indicating that the multi-spectral feature-level fusion model based on fluorescence emission spectrum data is more accurate and more effective for predicting COD of water quality.