Pathogenic Microbes Growth Pattern Analysis in Staple Suya Meat Consumption Using the Bayesian Networks

Abstract

Suya is a spicy meat skewer popular as a food item in the sub-Sahara regions. It is traditionally prepared by the Hausa people of
northern Cameroon, Nigeria, Niger, Ghana, and some parts of Sudan. Suya is generally made with skewered beef, ram, chicken
and innards such as kidney, liver and tripe. Being a stable meat caviar for people of this region, prepared for consumption and in
almost all cases, very unhygienic and sanitary conditions, the beef regalement is exposed massively to microbiome especially
Escherichia coli (E. coli). Though most E.coli strains are harmless, some can cause serious food poisoning and severe disease.
This paper aims to adopt the generality of Bayesian networks to represent and solve decision problems under uncertainty using
influence diagrams from Bayesian taxonomies. It also investigates the application of Bayesian networks principles in making
reliable predictions about reoccurrence of microbe growths. Based on the analytical categorisation, the research work builds on
extant historical trends which establish referential patterns for microbiome growth in suya meat using Bayesian Networks as a
formal model for characterizing and reasoning with uncertain information. Bayesian network models have proven useful in
Decision Support Systems (DSS) and in many fields of Artificial Intelligence (AI) including medical domain diagnosis and
weather forecasting. This research extends the use of Bayesian networks and analysis in the interdisciplinary studies of
information system in microbiology