Laser & Optoelectronics Progress, Vol. 55, Issue 11, 113003 (2018)
Detection of Chemical Oxygen Demand in Water Based on Multi-Spectral Fusion of Ultraviolet and Fluorescence
Zhou Kunpeng1, Bai Xufang1, and Bi Weihong2,*
- 1 College of Physics and Electronic Information, Inner Mongolia University for Nationalities, Tongliao, Inner Mongolia 0 28000, China
- 2 Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 0 66004, China
A method for detecting chemical oxygen demand (COD) in water based on ultraviolet and fluorescence multi-spectral fusion is proposed. The experimental samples are 53 actual water samples, including coastal seawater and surface water. The physicochemical values of the experimental samples are calculated by the standard chemical method, and the ultraviolet absorption spectra of the samples are collected by the ultraviolet-visible spectrometer, and the three-dimensional fluorescence spectra are collected by fluorescence spectrophotometer, then the processed spectral data are used to build model. Using the ant colony-interval partial least squares(ACO-iPLS) as feature extraction algorithm and the particle swarm optimization least squares support vector machine(PSO-LSSVM) as modeling method, we establish the prediction model based on ultraviolet absorption spectra and fluorescence emission spectra at single excitation wavelength, the data level fusion model and the feature level fusion (mid-level data fusion, MLDF) model based on ultraviolet and fluorescence multi-spectral information, respectively. And the prediction results of various models are compared. The results show that the prediction effect of the MLDF model based on ultraviolet and fluorescence multi-spectral information is optimal, and the prediction accuracy of COD in water is relatively high. The determination coefficient of calibration set is 0.9999, the prediction determination coefficient of validation set is 0.9912, and the root mean square error in prediction set is 1.1297 mg/L. It provides a new research idea and solution for the rapid detection of COD in water.