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Abstract
Microbial food spoilage is responsible for a considerable amount of waste and can cause food-borne diseases in humans, particularly in immunocompromised individuals and children. Therefore, preventing microbial food spoilage is a major concern for health authorities, regulators, consumers, and the food industry. However, the contamination of food products is difficult to control because there are several potential sources during production, processing, storage, distribution, and consumption, where microorganisms come in contact with the product. Here, we use high-throughput full-length 16S rRNA gene sequencing to provide insights into bacterial community structure throughout a pork-processing plant. Specifically, we investigated what proportion of bacteria on meat are presumptively not animal-associated and are therefore transferred during cutting via personnel, equipment, machines, or the slaughter environment. We then created a facility-specific transmission map of bacterial flow, which predicted previously unknown sources of bacterial contamination. This allowed us to pinpoint specific taxa to particular environmental sources and provide the facility with essential information for targeted disinfection. For example, Moraxella spp., a prominent meat spoilage organism, which was one of the most abundant amplicon sequence variants (ASVs) detected on the meat, was most likely transferred from the gloves of employees, a railing at the classification step, and the polishing tunnel whips. Our results suggest that high-throughput full-length 16S rRNA gene sequencing has great potential in food monitoring applications.
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1 University of Veterinary Medicine, Institute of Food Safety, Food Technology and Veterinary Public Health, Vienna, Austria (GRID:grid.6583.8) (ISNI:0000 0000 9686 6466); Safety and Innovation FFoQSI GmbH, Austrian Competence Centre for Feed and Food Quality, Tulln, Austria (GRID:grid.6583.8)
2 University of Veterinary Medicine, Institute of Food Safety, Food Technology and Veterinary Public Health, Vienna, Austria (GRID:grid.6583.8) (ISNI:0000 0000 9686 6466)
3 University of Vienna, Department of Analytical Chemistry, Vienna, Austria (GRID:grid.10420.37) (ISNI:0000 0001 2286 1424)