Quantitative Analysis of Deltamethrin Residues in Water Using Surface-Enhanced Raman Spectroscopy

The wide use of deltamethrin pesticides has become a major public health issue of global concern. Due to the colorless and odorless nature of trace deltamethrin in water, the quantitative detection of its residue faces significant technical challenges. This study innovatively combines surface-enhanced Raman spectroscopy (SERS) with enhanced deep neural networks and proposes a highly sensitive and accurate quantitative analysis method for deltamethrin. The traditional CNN model was structurally strengthened by introducing the gated recurrent unit (GRU) and the Attention mechanism, and a CNN-GRU-Attention enhanced hybrid neural network was constructed. Experimental comparisons show that this enhanced model is significantly superior to the traditional PLSR, SVM and basic CNN methods in terms of feature extraction ability and nonlinear relationship modeling. Its prediction results achieve excellent performance with R² = 0.9827 and RMSE = 0.3896. Compared with the traditional detection methods, the accuracy is increased by 8 % and the error is reduced by 40 %. Research shows that the fusion strategy based on SERS and enhanced deep learning, through multi-dimensional feature attention focusing and time-dependent modeling, effectively breaks through the sensitivity bottleneck of traditional analytical methods in trace pesticide detection, providing an innovative technical path for the precise monitoring of environmental pollutants.