Research on Correction Method of Water Quality Ultraviolet-Visible Spectrum Data Based on Compressed Sensing

Abstract

The turbidity interference caused by suspended particles in water seriously affects the accuracy of ultraviolet-visible spectroscopy in detecting water quality chemical oxygen demand. Based on this, the application of ultraviolet-visible spectroscopy to detect water quality chemical oxygen demand usually requires physical and mathematical methods to correct the spectral baseline interference caused by turbidity. Because of the slow response speed and unstable compensation effect of traditional correction methods, this paper proposes to use a compressed sensing algorithm to perform baseline correction and achieve good results. In the experiment, we selected formazin turbidity solution and sodium oxalate standard solution and carried out the research on the algorithm of turbidity correction for detecting chemical oxygen demand of water quality by ultraviolet-visible spectroscopy. The experiment obtains the absorption spectra of different concentrations of formazine turbidity solutions and the same concentration of sodium oxalate with different turbidity standard solutions at 210∼845 nm and analyzes the nonlinear effect of absorbance on turbidity. This article uses standard solution experiments to explore the compressed sensing theory for turbidity correction, and through the correction of the absorption spectrum of the actual water sample, it verifies the feasibility of the compression theory for turbidity correction. The method effectively corrects the baseline shift or drift of the water quality ultraviolet-visible absorption spectrum caused by suspended particles, while retaining the absorption characteristics of the ultraviolet spectrum, and it can effectively improve the accuracy and accuracy of the ultraviolet-visible spectroscopy water quality chemical oxygen demand detection.