Zero-inflated regressions for modelling microbial low prevalence and sampling performance for foodborne pathogens
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abstract
Microbial contamination of raw poultry meat could occur because of improper handling at primary production
and slaughterhouse levels. Low microbial prevalence data often consists of a high amount of non-detections
(zero positives), so a flexible framework is required to characterise the underlying microbial distribution and
conduct reliable inferential statistics. Thus, the objective of this work was to evaluate the performance of zeroinflated
binomial (ZIB) regression models to describe the effects of sampling site (carcass, thigh, breast, wings)
on the measured incidences of Salmonella, Listeria monocytogenes and Staphylococcus aureus on chicken
meat. For this aim, a number of fixed- and random-effects models were evaluated and compared, while
sampling performance based on mean prevalence estimates was assessed.
