Bayesian inference for quantifying Listeria monocytogenes prevalence and concentration in minced pork meat from presence/absence microbiological testing

Abstract

The purpose of this work was to estimate the prevalence and concentration of Listeria monocytogenes in minced pork meat by the application of a Bayesian modeling approach. Samples (n = 100) collected from local markets were tested for L. monocytogenes using in parallel the PALCAM, ALOA and RAPID'L.mono selective media. Presence of the pathogen was confirmed through biochemical and molecular tests. Independent experiments (n = 10) for validation purposes were performed. No L. monocytogenes was enumerated by direct-plating (<10 CFU/g), though the pathogen was detected in 22% of the samples. Sensitivity and specificity varied depending on the culture method. L. monocytogenes concentration was estimated at 14-17 CFU/kg. Validation showed good agreement between observed and predicted prevalence (error = -2.17%). The use of at least two culture media in parallel enhanced the efficiency of L. monocytogenes detection. Bayesian modeling may reduce the time needed to draw conclusions regarding L. monocytogenes presence and the uncertainty of the results obtained.