Food safety continues to threaten public health. Machine learning holds potential in
leveraging large, emerging data sets to improve the safety of the food supply and mitigate …

One of the main challenges in food microbiology is to prevent the risk of outbreaks by
avoiding the distribution of food contaminated by bacteria. This requires constant monitoring …

Salmonella Typhimurium is associated with foodborne diseases worldwide, including in
Peru, and its emerging antibiotic resistance (AMR) is now a global public health problem …

Background Genomic data-based machine learning tools are promising for real-time
surveillance activities performing source attribution of foodborne bacteria such as Listeria …

The purpose of the study was to apply a Bayesian source attribution model to England and
Wales based data on Salmonella Typhimurium (ST) and monophasic variants (MST), using …

Non-typhoidal Salmonella is an important causative agent of diarrheal illness worldwide. A
systematic review and meta-analysis of case-control studies were performed to determine …

Methodologies for source attribution (SA) of foodborne illnesses comprise a rapidly
expanding suite of techniques for estimating the most important source or sources of human …

Salmonella is one of the main causes of human foodborne illness. It is endemic worldwide,
with different animals and animal-based food products as reservoirs and vehicles of …

In response to the massive use of biocides for controlling Listeria monocytogenes (hereafter
Lm) contaminations along the food chain, strains showing biocide tolerance emerged. Here …

Food safety has a major correlation with health related to the public. Machine learning can
be a great help for large volume and emerging data sets to enhance the safety of the food …