Development of an Intelligent Visual SEA Detection Method for Salmonella

Objective: This study aimed to develop a rapid and visual method for detecting Salmonella detection by integrating strand exchange amplification (SEA) with a smartphone-based application (APP). Methods: A chitosan-modified silica membrane (CMSM) was optimized for nucleic acid enrichment from samples. Primers for the SEA isothermal amplification reaction were designed, and a smartphone application "Salmonella Smart Detection" was developed based on neutral red coloration. The specificity, sensitivity, anti-interference capability, and detection performance for artificially contaminated samples were systematically evaluated. Results: The detection platform exhibited high specificity for Salmonella. The detection limit for genomic DNA was 10 pg/μL, while the sensitivity for bacterial colony detection was 1.5×102 CFU/mL. The method demonstrated strong resistance to interference from natural background bacterial flora in pork, achieving a detection sensitivity of 3.57×102 CFU/mL in artificially contaminated pork samples. Conclusion: The intelligent visual detection system based on SEA developed in this study demonstrates rapid performance, high sensitivity, strong specificity, and advanced intelligent visualization capabilities. This system offers an innovative and practical approach for on-site visual screening of Salmonella in food products.