Bayesian modeling of two-species bacterial competition growth and decline rates in milk

TL;DR

This study employs Bayesian modeling to analyze the complex interactions between Escherichia coli O157:H7, non-pathogenic E. coli, and Pseudomonas fluorescens in milk, revealing insights into bacterial growth dynamics relevant for food safety.

Contribution

It introduces a Bayesian approach to model two-species bacterial interactions in milk, highlighting the complexity and potential for using microbiota to control pathogens.

Findings

No direct correlation between Pseudomonas growth rate and E. coli population density.

Complex interactions observed between bacterial species in milk.

Modeling approach can inform food safety strategies.

Abstract

Shiga toxin-producing Escherichia coli O157: H7 is a food-borne pathogen and the major cause of hemorrhagic colitis. Pseudomonas is the genus most frequent psychrotrophic spoilage microorganisms present in milk. Two-species bacterial systems with Escherichia coli O157: H7, non-pathogenic Escherichia coli, and Pseudomonas fluorescens in skimmed milk at 7, 13, 19, or 25 C was studied. Bacterial interactions were modelled after applying a Bayesian approach. No direct correlation between Pseudomonas fluorescent's growth rate and its effect on the maximum population densities of Escherichia coli species was found. The results show the complexity of the interactions between two species into a food model. The use of natural microbiota members to control foodborne pathogens could be useful to improve food safety during the processing and storage of refrigerated foods.