A hierarchical Bayesian approach to estimate the most probable number (MPN) concentration of Salmonella in raw chicken from qualitative data

The Quick Summary

This study uses a special math trick to figure out how much bad bacteria, called Salmonella, might be in raw chicken. It’s like counting how many germs are there, even when it’s hard to find them all. This helps us understand the risk of getting sick from eating chicken better.

Practical Implications

This study provides a more accurate and robust method for estimating Salmonella concentrations in food, even with limited qualitative data. This advancement is critical for improving quantitative microbial risk assessments and developing more effective control strategies in the food industry.

Potential Use in Indonesia

In Indonesia, where traditional markets and street food vendors are common and tropical climates can accelerate bacterial growth, this method could significantly improve food safety surveillance. By more accurately assessing Salmonella levels in local chicken supply chains, authorities can better target interventions at high-risk points. This could lead to a reduction in foodborne illnesses caused by Salmonella for Indonesian consumers.

Original Abstract

Estimating bacterial concentrations in food products is a key element in quantitative microbial risk assessment (QMRA). A Bayesian probabilistic approach with the most probable number (MPN) technique was developed to estimate the concentration of Salmonella in chicken meat based on the qualitative data. Salmonella contamination within a batch of food samples tends to lack homogeneity, and a zero-inflated distribution, i.e., zero-inflated Poisson (ZIP) or zero-inflated negative binomial (ZINB)……

Read the full peer-reviewed publication here