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

Area of Science:

• Environmental Science
• Analytical Chemistry
• Biotechnology

Background:

• Deltamethrin pesticide use poses global public health risks.
• Trace deltamethrin in water is difficult to detect due to its colorless and odorless nature.
• Accurate quantitative detection of pesticide residues remains a significant technical challenge.

Purpose of the Study:

• To develop a highly sensitive and accurate quantitative analysis method for trace deltamethrin.
• To overcome the limitations of traditional analytical methods for pesticide detection.
• To provide an innovative technical solution for environmental pollutant monitoring.

Main Methods:

• Integration of surface-enhanced Raman spectroscopy (SERS) with enhanced deep neural networks.
• Development of a CNN-GRU-Attention hybrid neural network for structural enhancement.
• Utilizing multi-dimensional feature attention focusing and time-dependent modeling.

Main Results:

• The enhanced CNN-GRU-Attention model significantly outperformed traditional methods (PLSR, SVM, basic CNN).
• Achieved excellent prediction performance with R² = 0.9827 and RMSE = 0.3896.
• Demonstrated an 8% increase in accuracy and a 40% reduction in error compared to traditional methods.

Conclusions:

• The fusion strategy of SERS and enhanced deep learning effectively addresses sensitivity limitations in trace pesticide detection.
• The proposed method offers an innovative technical pathway for precise environmental pollutant monitoring.
• This approach significantly improves the accuracy and sensitivity of deltamethrin residue analysis.