A new approach may be able to reduce by more than half the time it takes health officials to identify Salmonella strains, according to researchers in Penn State’s College of Agricultural Sciences.
The finding may significantly speed up the response to many outbreaks of foodborne illness, allowing epidemiological investigators to identify the exact strains of Salmonella that make people sick and to more quickly find — and eliminate — the source of the disease.
Working in collaboration with Carol Sandt, a scientist with the Bureau of Laboratories, Division of Clinical Microbiology in the Pennsylvania Department of Health and Eija Trees, a microbiologist at the U.S. Centers for Disease Control and Prevention, Shariat used Salmonella samples supplied by the state health department. Results of the study were published online in May in the Journal of Clinical Microbiology.
“Compared to the current method being used nationally and internationally to subtype Salmonella, our approach is faster,” Shariat said. “The significance of that is you need to trace the source of an outbreak as quickly as you can before you start insisting on restaurant and farm closures. It is important to pinpoint the source of the bacteria — the quicker you do that the quicker you can respond to the disease outbreak.”
This document describes the development of a tool to manage the risk of the transportation of cold food without temperature control. The tool uses predictions from ComBase predictor and builds on the 2009 U.S. Food and Drug Administration Model Food Code and supporting scientific data in the Food Code annex. I selected Salmonella spp. and Listeria monocytogenes as the organisms for risk management. Salmonella spp. were selected because they are associated with a wide variety of foods and grow rapidly at temperatures >17°C. L. monocytogenes was selected because it is frequently present in the food processing environment, it was used in the original analysis contained in the Food Code Annex, and it grows relatively rapidly at temperatures <17°C. The suitability of a variety of growth models under changing temperature conditions is largely supported by the published literature. The ComBase predictions under static temperature conditions were validated using 148 ComBase database observations for Salmonella spp. and L. monocytogenes in real foods. The times and temperature changes encompassed by ComBase Predictor models for Salmonella spp. and L. monocytogenes are consistent with published data on consumer food transport to the home from the grocery store and on representative foods from a wholesale cash and carry food service supplier collected as part of this project. The resulting model-based tool will be a useful aid to risk managers and customers of wholesale cash and carry food service suppliers, as well as to anyone interested in assessing and managing the risks posed by holding cold foods out of temperature control in supermarkets, delis, restaurants, cafeterias, and homes.
