Modelling Salmonella concentration throughout the pork supply chain by considering growth and survival in fluctuating conditions of temperature, pH and a w Carmen Pin a, ⁎, Gaspar Avendaño-Perez a , Elena Cosciani-Cunico b , Natalia Gómez a , Antonia Gounadakic b , George-John Nychas c , Panos Skandamis c , Gary Barker a a Institute of Food Research, Norwich, NR4 7UA, United Kingdom b Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna, via Bianchi 7, 25100, Brescia, Italy c Department of Food Science and Technology, Agricultural University of Athens, Greece abstract article info Keywords: Salmonella Pork supply chain Predictive models We aim to predict the population density of Salmonella spp. through the pork supply chain under dynamic environmental conditions (pH, a w and temperature) that fluctuate from growth to survival/slow inactivation. To do this, the dependence of the probability of growth, and of the growth and inactivation rate on the temperature, pH and a w were modelled. Probabilistic and kinetic measurements, i.e. growth and survival curves, were collected from the ComBase database (www.combase.cc). Conditions at which selected data used to fit the models were generated covered wide ranges that are relevant to the pork supply chain. Probabilistic and kinetic models were combined to give predictions on the concentration of Salmonella spp. at any stage of the pork supply chain under fluctuating pH, a w and/or temperature. Models were implemented in a user-friendly computing tool freely available from http://www.ifr.ac.uk/safety/SalmonellaPredictions/. This program provides estimates on the population dynamics of Salmonella spp. at any stage of the pork supply chain and its predictive performance has been validated in several pork products. © 2010 Elsevier B.V. All rights reserved. 1. Introduction Salmonella enterica is one of the most common bacterial causes of food-borne gastroenteritis in humans. In the United States, Salmonella is responsible for an estimated 1.2 million illnesses annually, only 40,000 of which are reported or clinically diagnosed, and an estimated 400 deaths are attributed to Salmonella infections each year (http:// www.cdc.gov/nczved/dfbmd/disease_listing/salmonellosis_gi.html). The majority of Salmonella infections are associated with the ingestion of contaminated meat and eggs as well as fresh produce and seasonings (Gomez et al., 1997). In Europe, it has been estimated that the consumption of contaminated pork and its products may account for 10% to 23% of the total number of cases of human salmonellosis (Hald and Wegener, 1999). Most European countries have established programs for the control of Salmonella on the pork supply chain in response to a European Union Zoonosis Directive (Anonymous, 2003). Salmonella is frequently detected in swine farms and slaughter pigs. In Ontario, Canada, Salmonella was recovered from 46% of the 80 grower–finisher pig farms visited during 6 months (Farzan et al., 2008). At the farm level, Salmonella organisms are able to survive in the paddock environment for several weeks, and even a low level of Salmonella contamination represents an infection risk for pigs in both organic and conventional production systems (Jensen et al., 2006). Truck-washing systems, waste-processing lagoons, holding pens and other sources, are potential contributors to the exposure and dissemination of Salmonella in commercial swine production systems (Dorr et al., 2009). Therefore, Salmonella is frequently detected in the abattoir. In Canada, Salmonella organisms were detected in 12.5% of cecal samples from slaughter pigs (Mainar-Jaime et al., 2008) and the contamination during slaughtering of 14% of carcases was reported (Letellier et al., 2009). One of the objectives of the BIOTRACER project (http://www. biotracer.org/) is to infer the source of Salmonella contamination when found in the pork supply chain. This requires developing and implementing a domain model for the hazards that are associated with Salmonella in pork including a systematic representation of the food chain structure and predictive models for the quantification of the kinetic response of the bacterial population to the environmental conditions. The kinetic response of a bacterial population to environmental conditions is uncertain and the presence of uncertain- ty is reflected in the difficulty associated with ‘tracing’ bacterial contamination. By decreasing the uncertainty associated with growth models (i.e. parameter uncertainty) there are opportunities for improved source level inference (e.g. http://biotracer.hugin.com/). Models to predict the growth of Salmonella in foods are available at several public sources (www.combase.cc; Pathogen Modelling Pro- gram http://ars.usda.gov/Services/docs.htm?docid=11550PMP) and International Journal of Food Microbiology 145 (2011) S96–S102 ⁎ Corresponding author. Institute of Food Research, Norwich Research Park, Norwich NR4 7UA, United Kingdom. Tel.: +44 1603 255000; fax: +44 1603 255288. E-mail address: carmenpin@bbsrc.ac.uk (C. Pin). 0168-1605/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.ijfoodmicro.2010.09.025 Contents lists available at ScienceDirect International Journal of Food Microbiology journal homepage: www.elsevier.com/locate/ijfoodmicro