Developing predictive based models to monitor the microbiological quality of beef meat processed for fast food restaurants

In just Europe each year, 2.3 million foodborne diseases are brought on by eating raw or undercooked meat. The majority of this ailment is linked to beef, which is a common ingredient in many traditional recipes around the world. Low (pathogenic) bacterial contamination during primary production and insufficient hygiene practices throughout the farm to fork chain are the causes of the low microbiological quality of beef. Therefore, this study uses an Artificial Neural Network (ANN) system to analyse the microbiological quality pathway of minced beef produced for fast food restaurants over a three year period. As one of the goods that spoils quickly and has a short shelf life, this simultaneous approach gave sufficient precision for the prediction of a microbiological profile of minced beef meat. For the first time, an ANN model was created to forecast the microbiological profile of beef mince in fast food restaurants based on the temperatures at which the meat is stored, the butcher's identity, and the time of day. Standardized microbiological tests that are advised for freshly processed meat were among the predicted problems. The developed prediction models, which had an overall r2 of 0.867 during the training cycle, can be used as a data source and as a tool to help the scientific community and regulatory agencies in charge of food safety identify particular monitoring and research needs.