Abstract
With an increasing focus on preharvest food safety, rapid methods are required for the detection and quantification of foodborne pathogens such as Salmonella enterica in beef cattle. We validated the Atlas Salmonella Detection Assay (SEN), a nucleic acid amplification technology that targets Salmonella rRNA, for the qualitative detection of S. enterica with sample enrichment using immunomagnetic separation as a reference test, and we further evaluated its accuracy to predict pathogen load using SEN signal-to-cutoff (SCO) values from unenriched samples to classify animals as high or nonhigh shedders. Rectoanal mucosal swabs (RAMS) were collected from 238 beef cattle from five cohorts located in the Midwest or southern High Plains of the United States between July 2015 and April 2016. Unenriched RAMS samples were used for the enumeration and SEN SCO analyses. Enriched samples were tested using SEN and immunomagnetic separation methods for the detection of Salmonella. The SEN method was 100% sensitive and specific for the detection of Salmonella from the enriched RAMS samples. A SEN SCO value of 8, with a sensitivity of 93.5% and specificity of 94.3%, was found to be an optimum cutoff value for classifying animals as high or nonhigh shedders from the unenriched RAMS samples. The SEN assay is a rapid and reliable method for the qualitative detection and categorization of the shedding load of Salmonella from RAMS in feedlot cattle.
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