摘要
Pseudomonas aeruginosa ( PA ) is an opportunistic pathogen that is widely distributed in soil, water, air, food, animal, and human body surfaces, and is also one of the main pathogens of iatrogenic infections which can cause various infections in people with weakened immune systems. The rapid detection of PA in varied environmental and clinical samples is important for hospital infection control, clinical diagnosis, and food and environmental safety monitoring. In this study, we constructed an efficient separation and enrichment surface-enhanced Raman scattering (SERS) platform based on a dual recognition strategy, and supplemented it with a machine learning nonlinear regression model to achieve rapid, highly sensitive, accurate, and specific detection of PA. By combining the SERS platform with support vector machine learning regression model, a detection limit of 15 CFU/mL with a liner range of 50 to 10 8 cells/mL was achieved. The highly sensitive dual-identification tag-type SERS quantitative analysis system established in this study has the advantages of wide detection range, low detection limit, good specificity, high accuracy and short detection time, thereby providing a method for the prevention and clinical diagnosis of PA infection-related diseases as well as food and environmental safety monitoring.
危险品化学品经营许可证（带存储）