基于紫外-可见光谱的水质TOC定量分析方法

地表水资源安全关系到国民健康、生态环境稳定和经济可持续发展，具有重要战略意义。总有机碳（TOC）是一种衡量水体中有机物含量的综合指标，其在水环境监管和治理中具有重要价值。传统检测方法通过高温催化氧化测定水样中TOC含量具有耗时较长、操作复杂的局限性，紫外-可见光谱技术具有检测速度快、操作简单的优势，因而在水质在线检测中具有较好的应用前景。国内外对地表水中TOC浓度的在线检测目前大多采用与COD浓度间的相关关系进行间接推算得到，这类方法对水体成分的稳定性要求较高。相比于常规的间接推算方法，采用光谱定量分析方法建立TOC与紫外-可见光谱间的分析模型具有更好的鲁棒性和分析精度，便于实现水质无人值守在线监测。实验配置了TOC样本溶液，设计了为期两天的实验，在4个时间段采集得到样品光谱数据集（分别记为D1，D2，…，D6）。首先，通过分组实验将D1作为训练集建立TOC偏最小二乘（PLS）回归模型，预测同一时间段测试集D2的TOC浓度，得到平均绝对相对误差（MAPE）不超过0.78%，表明建立的TOC定量分析模型具有较高的精度。然后，为验证PLS建立的TOC模型对仪器状态变化的鲁棒性，选择不同时间段采集的光谱数据分别作为训练集和测试集，进行不同仪器状态交叉实验，4组实验中测试集样品TOC浓度预测值的MAPE分别为3.82%，3.75%，3.43%和0.98%。实验表明，采用PLS算法建立的TOC紫外-可见光谱定量分析模型具有较好的分析精度和鲁棒性，分组实验和不同仪器状态交叉实验中预测浓度的MAPE均不超过3.82%，优于常规的间接推算法。此外，建立的光谱定量分析模型不依赖COD与TOC间的推算关系，因此在水环境变化时较常规推算方法具有更好的适应能力。最后，PLS算法建模过程简单，运算速度快，为浸入式在线检测设备的开发和维护提供了便利。

Quantitative Analysis of TOC in Water Quality Based on UV-Vis Spectroscopy

The safety of surface water resources is of great strategic significance. It is related to national health, ecological environment stability and sustainable economic development. Total organic carbon (TOC) is a comprehensive index to reflect the content of organic matter in water. Hence, it has significant value in water environment supervision and treatment. This method is time-consuming and complex. UV-Vis spectroscopy technology has the advantages of fast detection speed and simple operation. Therefore it has a good application prospect in online detection of water quality. At present, the online detection methods of TOC in surface water mostly are indirectly calculated at home and abroad. These methods depend on the correlation between the concentration of COD and TOC, and they require high stability of water composition. Compared with the indirect calculation methods, the spectral quantitative analysis method has better robustness and accuracy. Moreover, this method is convenient for realizing unattended online monitoring of water quality. The experiment was equipped with TOC sample solutions, and a two-day experiment was designed. Six spectral data sets of the samples (denoted as D1, D2, …, D6) were collected in 4 time periods. Firstly, D1 was used as the training set to establish a partial least squares (PLS) regression model in the group experiment. This model was used to predict the TOC concentration of D2, and the mean absolute percentage error (MAPE) was less than 0.78%. In addition, D1 and D2 were collected in the same period. The results show that the established TOC quantitative analysis model has high accuracy. Then, to verify the robustness of the TOC model established by the PLS method to the change of instrument state, the spectral data collected in different periods were selected as the training set, the test set and the validation set. Furthermore, the cross experiments of different instrument states were performed. The MAPE of the predicted TOC concentration in the four experiments were 3.82%, 3.75%, 3.43% and 0.98%, respectively. The results show that the UV-Vis spectroscopy quantitative analysis model of TOC established by the PLS algorithm has good accuracy and robustness. The MAPE of predicted concentration in the grouping experiment and cross experiments of different instrument states are all less than 3.82%. These results are better than the conventional indirect calculation method. Moreover, the established spectral quantitative analysis model does not depend on the calculation relationship between COD and TOC. Thus, it has better adaptability than the conventional indirect calculation method when the water environment changes. Finally, the PLS algorithm has the advantages of a simple modeling process and fast operation speed. It provides convenience for the development and maintenance of submersible online detection equipment.