Multidimensional information fusion and analysis of ultraviolet absorption spectroscopy: Simultaneous detection method for copper and zinc ion concentrations in seawater

Heavy metal pollution poses a significant threat to marine ecological environments and serves as a crucial indicator for seawater quality assessment. The enrichment of heavy metals severely impacts marine ecosystem health and poses threats to human food safety, underscoring the critical importance of monitoring heavy metal levels in marine waters. In this paper, UV absorption spectroscopy was used to collect the spectra of seawater containing different concentrations of Cu and Zn ions, which overcomes the problems of traditional methods that require extensive pre-treatment and lossy samples. To address the characteristics of the data, the Savitzky-Golay (SG) filtering algorithm is used for preprocessing. The acquired spectra are then subjected to further data analysis using the proposed multidimensional information fusion method called SpectraNet Fusion Algorithm (SFA) for UV absorption spectroscopy. The local feature information extraction module and the whole domain information feature extraction module were respectively used to predict the regression of the data in parallel two-channel, and the output results were integrated to form a new feature map, which was further processed by the feature information fusion module, and finally output the prediction results corresponding to the spectra to realize the accurate detection of Cu and Zn heavy metal ions in seawater. The coefficient of determination (R²) of the model training set established by this method reaches 98.00 %, the root mean square error (RMSE) reaches 0.0389, and the mean absolute error (MAE) reaches 0.0243, combined with the experimental design, the detection range was 5 μg·L⁻¹ to 90 μg·L⁻¹,which realizes simultaneous and high-precision prediction of Cu and Zn heavy metal ions in seawater.