Improving beef safety: development of a semi-mechanistic predictive model to assess the transfer of Salmonella in the production chain, with further validation in pilot scale

• Grant number: 11/18499-6
• Support Opportunities: Regular Research Grants
• Start date: March 01, 2012
• End date: February 28, 2014
• Field of knowledge: Agronomical Sciences - Food Science and Technology - Food Science
• Principal Investigator: Bernadette Dora Gombossy de Melo Franco
• Grantee: Bernadette Dora Gombossy de Melo Franco
• Host Institution: Faculdade de Ciências Farmacêuticas (FCF). Universidade de São Paulo (USP). São Paulo , SP, Brazil

Abstract

Beef cattle industry represents a major portion of the Brazilian agribusiness, accounting for more than R$ 50 billion per year in incomes and 7.5 million jobs. Several studies have demonstrated the prevalence of Salmonella in all steps of beef production chain, which is worrisome as Salmonella is a pathogen of major importance for foods worldwide and in Brazil, due to the large economic losses and public health problems associated to this pathogen. The mitigation of the issues associated with the presence of Salmonella in beef products will be more efficient if the transfer of this pathogen during beef processing is better known. Few studies assessed the transfer of Salmonella in the food production chain, despite of the recognized importance and association as a common cause of foodborne disease outbreaks. While growth and inactivation of foodborne pathogens have been extensively represented in quantitative risk assessment models, the phenomena of Salmonella transfer has been rarely considered. Therefore, the current project will investigate the transfer of Salmonella spp. during the grinding of beef, by means of challenge tests, and based on the generated data, develop a semi-mechanistic predictive model, which will be further validated under semi-industrial conditions. The development of a predictive model capable of predicting the transfer of Salmonella spp. even in low concentrations is of paramount importance to ensure its practical application and consequently, to contribute with actions aiming at improving the microbiological safety of fresh beef. The developed model will be also useful to predict the transfer of Salmonella spp. in the production of other types of meat (poultry, pork) and in other steps of the production chain. (AU)