Development and application of a visual loop-mediated isothermal amplification combined with lateral flow dipstick (LAMP-LFD) method for rapid detection of Salmonella strains in food samples

Abstract

Salmonella strains are major foodborne pathogens for the animals and humans, presenting a significant threat to food safety and public health worldwide. The rapid and accurate diagnosis of Salmonella infection is required for effective control and management of disease epidemics. In this study, we developed a rapid and efficient assay that combines loop-mediated isothermal amplification with lateral flow dipstick (LAMP-LFD) for detection of the Salmonella hilA gene. Compared with PCR and real-time PCR methods, the LAMP-LFD assay has the same specificity and higher sensitivity, and required only 40 min (10 min for LFD detection) at 65 °C. All 52 strains of Salmonella yielded positive results using the LAMP-LFD assay and showed no cross reaction with 37 tested non-Salmonella strains. The detection limit of the LAMP-LFD assay was 13.5 fg/μl (genomic DNA) and 6.7 CFU/mL (cell), which was 1000-fold more sensitive than conventional PCR and 100-fold more sensitive than real-time PCR. Additionally, the LAMP-LFD method could detect Salmonella in artificially contaminated food samples (milk, pork, beef and chicken) when present as low as 144 CFU/mL or CFU/g and without the use of an enrichment step. Nevertheless, the sensitivity was increased to 1.44 CFU/mL or CFU/g after culturing at 37 °C for 6 h. Fifty food samples (chicken meat) were used to test the practicality of the LAMP-LFD assay. After an enrichment step at 37 °C for 6 h, the results showed 100% accuracy compared to the standard culture-based method (ISO 6579:2002) in which 17 out of 50 food samples gave positive results. Overall, the results demonstrated that the developed LAMP-LFD method targeting the hilA gene is rapid, specific, sensitive and allows ease of operation for Salmonella detection, suggesting the potential for this assay to be used as an alternative to traditional testing methods and could applied in low-resource settings.