【摘要】 快速、精准地实现水体中多种污染物的耦合干扰解析及含量检测对野外水质实时监测具有重要意义。针对紫外光谱法同步检测化学需氧量（COD）和浊度时存在特征耦合及谱峰重叠干扰，进而严重影响检测精度的问题，提出了一种连续投影算法结合支持向量回归的水质污染物含量解耦预测方法。采用连续投影算法对水质样本的紫外吸收光谱特征波长进行筛选，消除无关冗余数据以提高模型迭代速率和精度。基于多分类支持向量机思想对支持向量回归算法进行多回归拟合改进，实现COD和浊度的紫外光谱耦合解析和含量的同步预测。通过实际水样检测验证，结果表明：耦合解析前的预测均方根误差改进率达到76%，最大相对误差均降低至4%以内，优于同类方法的检测精度，该研究对紫外光谱法水质多耦合参数检测应用具有参考价值。更多还原

【Abstract】 The rapid and accurate coupling interference analysis and concentration detection of the multiple pollutants in complex water bodies are significantly important for the in-situ real-time monitoring of field water quality. To address the problems of characteristic coupling and the interference of spectral peaks in the synchronous detection of chemical oxygen demand(COD) and turbidity using ultraviolet spectroscopy, which significantly affect the detection accuracy, a decoupling method for predicting water pollutant concentration was developed in this study based on the continuous projection algorithm combined with support vector regression. The continuous projection algorithm was used to screen the characteristic wavelengths of the ultraviolet absorption spectra of water quality samples and eliminate irrelevant redundant numbers, to improve the iteration rate and accuracy of the model. Based on the concept of the multi-classification support vector machine, the support vector regression algorithm was improved via multi-regression fitting, and the ultraviolet coupling analysis of COD and turbidity, as well as the simultaneous prediction of concentration, was realized. The test results for actual water samples reveal that the maximum relative errors are reduced to less than 4%, and the improvement rate of the root mean square error of predictions before the coupling analysis reaches 76%. Thus, the proposed method offers a better detection accuracy, as compared with similar methods. Notably, this work is expected to serve as a reference for the application of ultraviolet spectroscopy in water-quality multi-coupling parameter detection.更多还原