Wang Hongliang, Xiang Houkui, Xiong Tongqiang, Feng Jinping, Zhang Jianquan, Li Xuemei
School of Automation, Hubei University of Science and Technology, Xianning, China.
Office of Laboratory Management and Teaching Facilities Development, Renmin University of China, Beijing, China.
Front Microbiol. 2023 Jul 10;14:1224207. doi: 10.3389/fmicb.2023.1224207. eCollection 2023.
Recently, ultraviolet-visible (UV-vis) absorption spectrometry has garnered considerable attention because it enables real-time and unpolluted detection of chemical oxygen demand (COD) and plays a crucial role in the early warning of emerging organic contaminants. However, the accuracy of detection is inevitably constrained by the co-absorption of organic pollutants and turbidity at UV wavelengths. To ensure accurate detection of COD, it is necessary to directly subtract the absorbance caused by turbidity from the overlaid spectrum using the principle of superposition. The absorbance of COD is confined to the UV range, whereas that of turbidity extends across the entire UV-vis spectrum. Therefore, based on its visible absorbance, the UV absorbance of turbidity can be predicted. In this way, the compensation for turbidity is achieved by subtracting the predicted absorbance from the overlaid spectrum. Herein, a straightforward yet robust exponential model was employed based on this principle to predict the corresponding absorbance of turbidity at UV wavelengths. The model was used to analyze the overlaid absorption spectra of synthetic water samples containing COD and turbidity. The partial least squares (PLS) method was employed to predict the COD concentrations in synthetic water samples based on the compensated spectra, and the corresponding root mean square error (RMSE) values were recorded. The results indicated that the processed spectra yielded a considerably lower RMSE value (9.51) than the unprocessed spectra (29.9). Furthermore, the exponential model outperformed existing turbidity compensation models, including the Lambert-Beer law-based model (RMSE = 12.53) and multiple-scattering cluster method (RMSE = 79.34). Several wastewater samples were also analyzed to evaluate the applicability of the exponential model to natural water. UV analysis yielded undesirable results owing to filtration procedures. However, the consistency between the compensated spectra and filtered wastewater samples in the visible range demonstrated that the model is applicable to natural water. Therefore, this proposed method is advantageous for improving the accuracy of COD measurement in turbid water.
近年来,紫外可见(UV-vis)吸收光谱法备受关注,因为它能够对化学需氧量(COD)进行实时且无污染的检测,在新兴有机污染物的预警中发挥着关键作用。然而,检测的准确性不可避免地受到有机污染物在紫外波长下的共吸收以及浊度的限制。为确保COD的准确检测,有必要利用叠加原理直接从叠加光谱中减去浊度引起的吸光度。COD的吸光度局限于紫外范围,而浊度的吸光度则延伸至整个紫外可见光谱。因此,基于其可见光吸光度,可以预测浊度的紫外吸光度。通过这种方式,从叠加光谱中减去预测的吸光度即可实现对浊度的补偿。在此,基于该原理采用了一种简单而稳健的指数模型来预测紫外波长下浊度的相应吸光度。该模型用于分析含有COD和浊度的合成水样的叠加吸收光谱。采用偏最小二乘法(PLS)根据补偿后的光谱预测合成水样中的COD浓度,并记录相应的均方根误差(RMSE)值。结果表明,处理后的光谱产生的RMSE值(9.51)比未处理的光谱(29.9)低得多。此外,指数模型优于现有的浊度补偿模型,包括基于朗伯-比尔定律的模型(RMSE = 12.53)和多重散射聚类法(RMSE = 79.34)。还分析了几个废水样本,以评估指数模型对天然水的适用性。由于过滤程序,紫外分析产生了不理想的结果。然而,补偿光谱与可见范围内过滤后的废水样本之间的一致性表明该模型适用于天然水。因此,该方法有利于提高浊水中COD测量的准确性。