三维荧光光谱在水体监测和水处理领域日益引起广大研究者的关注。 自组织映射神经网络(SOM网络)作为一种非监督、 自学习的神经网络, 具有自稳定性高、 抗噪声能力强等特点。 使用SOM网络对某自来水厂处理流程中水样的荧光光谱进行解析, 可以将三维荧光光谱聚类成三类, 分别对应为络氨酸类蛋白有机物、 色氨酸类蛋白有机物、 紫外富里酸类物质。 整个自来水处理工艺能够有效的去除水体中的有机物, 其中络氨酸类、 色氨酸类、 紫外富里酸类物质的去除率分别为84.6%, 79.9%, 69.1%。 研究结果表明, SOM网络可以作为一种有效的水体荧光光谱分析工具, 有助于优化水处理工艺参数, 提高水处理工艺性能、 以及自来水厂的监测和管理。

Three-dimensional excitation and emission matrix fluorescence spectra (3D-EEM) has attracted the increasing attention of researchers in water monitoring and water treatment areas. The self-organizing map (SOM) is a kind of non-supervised and self-learning neural network with the feature of high self-stability and noise tolerance. In the present paper, SOM technique was employed for the exploratory analysis of EEM spectra of water samples in a water treatment plant. The results showed that EEM spectra could be clustered into three classes, corresponding to tryptophan-like protein substances, tyrosine-like protein substances, and UV fulvic-like substances. The three components could be effectively removed during the whole water treatment process with the high removal of 84.6% (tyrosine-like), 79.9% (tryptophan-like), and 69.1% (UV fulvic-like). The results show that SOM technique can be used as an effective tool for EEM spectra analysis, which is helpful for the optimization of water treatment process parameters, the improvement of process performance, and the operation of water treatment plant.