Quantitative detection of phenol red by surface enhanced Raman spectroscopy based on improved GA-BP

Phenol red (PR) is generally used as an acid-base indicator and a printing and dyeing colorant. When its content exceeds a certain concentration in water, it will cause great damage to the human body. Therefore, it is very important to detect the content of PR in water. The advantage of surface enhanced Raman scattering (SERS) is detecting samples quickly, non-destructive and high sensitivity without sample pre-treatment. SERS has attracted great attention in all fields of detection and analysis. In this paper, the method of attaching silver nanoparticles to metallic single-walled carbon nanotubes form carbon nanotubes/silver nanoparticles (CNTs/AgNPs) structure and then combining it with silica sheet is proposed. SERS substrate with silica/carbon nanotubes/silver nanoparticles (SiO₂/CNTs/AgNPs) composite structure has extremely high reinforcement effect. In the quantitative analysis of the detected substance, mathematical fitting or machine learning is used to find the relationship between the intensity of Raman signal and the concentration of the detected substance. The BP neural network optimized by genetic algorithm (GA-BP) is designed in this study. The weights of GA-BP to enhance the robustness of BP neural network, the method of adaptive learning rate and the number of hidden nodes is set to solve the problem that GA-BP is easy to fall into local optimum, thus establishing a quantitative analysis model of PR solution concentration. The model can detect different concentrations of PR solutions with high accuracy quickly, simply and sensitively. Finally, compared with other published quantitative models, GA-BP correlation coefficient R² determined by the training results of the model is 0.99996, and the root mean square error of the prediction is RMSEP = 0.002510, which is 0.0005 higher than the mathematical fitting method, it shows better performance. A reliable idea for the preparation of SERS substrate and online detection of PR concentration in water proposed in this study.