Title of article :
Prevalence, level and distribution of Salmonella in shipments of imported capsicum and sesame seed spice offered for entry to the United States: Observations and modeling results Original Research Article
Author/Authors :
Jane M. Van Doren، نويسنده , , Robert J. Blodgett، نويسنده , , Régis Pouillot، نويسنده , , Ann Westerman، نويسنده , , Daria Kleinmeier، نويسنده , , George C. Ziobro، نويسنده , , Yinqing Ma، نويسنده , , Thomas S. Hammack، نويسنده , , Vikas Gill، نويسنده , , Martin F. Muckenfuss، نويسنده , , Linda Fabbri، نويسنده ,
Abstract :
In response to increased concerns about spice safety, the United States Food and Drug Administration (FDA) initiated research to characterize the prevalence and levels of Salmonella in imported spices. 299 imported dried capsicum shipments and 233 imported sesame seed shipments offered for entry to the United States were sampled. Observed Salmonella shipment prevalence was 3.3% (1500 g examined; 95% CI 1.6–6.1%) for capsicum and 9.9% (1500 g; 95% Confidence Interval (CI) 6.3–14%) for sesame seed. Within shipment contamination was not inconsistent with a Poisson distribution. Shipment mean Salmonella level estimates among contaminated shipments ranged from 6 × 10−4 to 0.09 (capsicum) or 6 × 10−4 to 0.04 (sesame seed) MPN/g. A gamma-Poisson model provided the best fit to observed data for both imported shipments of capsicum and imported shipments of sesame seed sampled in this study among the six parametric models considered. Shipment mean levels of Salmonella vary widely between shipments; many contaminated shipments contain low levels of contamination. Examination of sampling plan efficacy for identifying contaminated spice shipments from these distributions indicates that sample size of spice examined is critical. Sampling protocols examining 25 g samples are predicted to be able to identify a small fraction of contaminated shipments of imported capsicum or sesame seeds.
Keywords :
Salmonella contamination , Spice , Enumeration , maximum likelihood estimation , Microbial contamination assessment