Various turbidity compensation methods have been developed to decrease or nearly eliminate turbidity interference resulting from suspended particles in water components ( Zhang et al., 2020). In addition, the inversion model of the ultraviolet-visible method has progressed from single wavelength and dual wavelength to the current multi-wavelength algorithm.Īlthough use of the multi-wavelength algorithm has resulted in a marked improvement in accuracy, turbidity-related interference still limits the accuracy of measurements, especially with regard to actual environmental water samples. More recently, a variety of ultraviolet-visible sensors have become commercially available employing multiple types of detectors, light sources, and optical light paths ( O'Grady et al., 2021). Johnson and Coletti developed a reflection-mode in situ ultraviolet spectrophotometer that can be immersed in ocean water to a depth of 400 m to measure nitrate, bisulfide, and bromide with high measurement efficiency ( Johnson and Coletti, 2002). Combined with other advantages such as high precision, high efficiency, and no secondary chemical pollution, ultraviolet-visible spectroscopy has been widely studied since its development by Langergraber’s group ( Langergraber et al., 2004). Ultraviolet-visible spectroscopy, a physical method based on Lambert-Beer’s law, is a rapid and cost-effective measurement method ( Guo et al., 2020). Chemical detection methods have many disadvantages, such as time-consuming sample preparation and operation steps, secondary pollution due to chemical reagents, requirements for expensive instrumentation, and complex training requirements for technicians ( Zulkifli et al., 2018). Both chemical and physical technologies are widely used to monitor water parameters ( Park et al., 2020 Yaroshenko et al., 2020). The United States Pharmacopoeia, Japanese Pharmacopoeia, and European Pharmacopoeia have chosen TOC as the quality standard test for water purity and water standard for injection ( Richard and Nissan, 2017). Total organic carbon (TOC) is a measure of the total amount of carbon in a water system or contaminants in purified water ( Otson et al., 1979). Indeed, studies have reported an increase in water pollution because of industrial development, agricultural activities, and incomplete domestic sewage treatment ( Englande et al., 2015 Sasakova et al., 2018). Experimental analyses showed that the 1D U-Net is suitable for turbidity compensation and provides accurate results.ĭue to economic growth and global climate change, the effects of environmental pollution, particularly water pollution, are becoming increasingly serious ( Ukaogo et al., 2020). After turbidity compensation, the R 2 between the predicted and true values increased from 0.918 to 0.965, and the RMSE (Root Mean Square Error) value decreased from 0.526 to 0.343 mg. Compared with orthogonal signal correction and extended multiplicative signal correction methods, the deep learning method specifically utilizes an accurate one-dimensional U-shape neural network (1D U-Net) and represents the first method enabling turbidity compensation in sampling real river water of agricultural catchments. This paper proposes a deep learning method to compensate for turbidity interference and obtain water parameters using a partial least squares regression approach. Suspended particles in water cause turbidity that interferes with the ultraviolet-visible spectrum and ultimately affects the accuracy of water parameter calculations. Ultraviolet-visible spectroscopy is an effective tool for reagent-free qualitative analysis and quantitative detection of water parameters. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China.Hongming Zhang Xiang Zhou Zui Tao* Tingting Lv Jin Wang Mizoguchi, et al., Thin Solid Films, 516, No.
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