Data fusion strategy based on ultraviolet–visible spectra and near-infrared spectra for simultaneous and accurate determination of key parameters in surface water

Abstract

Chemical oxygen demand (COD), ammonia nitrogen (AN) and total nitrogen (TN) are the key parameters to reflect the degree of surface water pollution. Ultraviolet – visible (UV–Vis) spectroscopy and near - infrared (NIR) spectroscopy are ideal techniques for rapid monitoring of these indicators. In this study, a strategy based on the fusion of UV–Vis and NIR spectral data (UV–Vis-NIR) for water quality detection was proposed to further improve the quantitative analysis accuracy of spectroscopic methods. Seventy river samples with different levels of pollution were used for spectroscopic analysis. The UV–Vis-NIR fusion spectrum of each water sample was obtained by directly splicing sample’s UV–Vis spectrum and NIR diffuse transmission spectrum. The UV–Vis-NIR fusion models were optimized through using different variable selection algorithms. The results show that the UV–Vis-NIR fusion models for surface water COD, AN and TN achieves better prediction results (the root mean square errors of prediction are 6.95, 0.195, and 0.466, respectively) than single-spectroscopic based models. Since better prediction performances were shown under different optimization conditions, the robustness of fusion models were also better than the single-spectroscopic based models. Therefore, the data fusion strategy proposed in this study has a promising application prospect for further accurate and rapid monitoring of surface water quality.