Spectral Analysis of Dissolved Organic Carbon in Seawater by Combined Reflectance and Fluorescence Technology

Dissolved organic carbon refers to soluble carbon substances in water bodies, and can be used as an important indicator for water pollution. Spectroscopic detection is commonly used to detect dissolved organic carbon in seawater. However, independent spectral methods are susceptible to interference, and insufficient extraction of the data features can occur. Accordingly, this study introduces a multi-source spectral fusion method that relies on a combination of principal component analysis and convolutional neural networks to construct the detection model. The Bayesian correction method is used for calibration, and the dissolved organic carbon contents of 10 groups of unfiltered seawater samples is analyzed. Correcting the spectral data acquired from samples containing impurities significantly improved the linear correlation coefficient R² of dissolved organic carbon from 0.8891 to 0.9838. Similarly, the mean absolute error is significantly reduced from 15.33% to 3.24%, while the individual absolute error is effectively controlled, remaining within 9%. The obtained results show that the developed method effectively integrates the ultraviolet absorption and fluorescence spectral data, and overcomes interference from other substances using the Bayesian correction method. Overall, this provides a highly accurate detection system with potential applications in monitoring the marine environment.