The goal of this project is to improve our ability to predict and control the behavior of Salmonella in fat-containing foods. This will be achieved by developing an understanding the role of lipid food components of different hydrophobicity and molecular weight in the survival of Salmonella. This project will provide improved predictions of inactivation of Salmonella in foods that have a history of transmitting salmonellosis. This research will provide a quantitative understanding of the factors that affect the ability of Salmonella to survive in foods that can be used in the development of risk assessments. In addition the project will increase our understanding of how fat-containing foods can be formulated to decrease the probability that Salmonella will survive in the product. The result of the project will be improved safety of fat-containing foods. <P>Specific objectives are: 1. Determine the influence of lipid hydrophobicity on the kinetics of Salmonella survival in fat-containing foods. 2. Determine the influence of fat-water interactions on the molecular mobility of water and their influence on the kinetics of Salmonella survival in foods. 3. Develop predictive models for Salmonella survival in fat-containing foods based on water activity and fat content. Validate these models using low moisture foods including peanut butter, chocolate, and powdered milk.
In the past 10 years there have been numerous outbreaks of salmonellosis associated foods of high fat content. Foods involved included chocolate, peanut butter and raw nuts, and powdered infant formula. The contamination of high fat foods with Salmonella is not a new problem; food industry microbiologists have known for decades that these foods are at risk for contamination with Salmonella. However, current practices appear inadequate to address the challenge, as contamination events continue, even in large multinational companies that employ experienced food microbiologists and risk managers. The presence of Salmonella in food is particularly hazardous to children and other high risk populations, as these foods protect Salmonella during passage through the stomach and even low doses (perhaps as few as 10 to 100 cells) pose a significant risk to health. Although we have a good understanding of how food components affect growth and survival of pathogens in intermediate and high moisture foods, there is no such fundamental understanding concerning how fat content affect survival. This is, in part, due to the reliance on water activity as a basis for understanding the role of the food matrix in Salmonella survival. Unfortunately, water activity is a poor predictor of microbial behavior in these foods. Fat content may play an important role in microbial survival in low moisture foods, but we lack a fundamental understanding of the basis for this role. This research will test the hypotheses that survival kinetics of Salmonella in foods is directly influenced by molecular structure of the fat which determines the extent to which lipids interact with water or other components of the food matrix. We propose that knowledge of lipid chemistry can be used to improve the ability to predict the behavior of Salmonella in foods.
Model foods will be formulated to have a range of fat contents and hydrophobicities. Each formulation will be equilibrated to water activities of 0.23, 0.33, and 0.43. Initially, two model foods will be used, one based on cocoa powder with added cocoa butter, and the other based on defatted peanut meal with added peanut oil. The influence of different lipids on water mobility will be determined by substituting portions of the products' natural fat (peanut oil or cocoa butter) with fats of different molecular structure. We will then select formulations that produce different hydrophobicities for use in Salmonella survival studies. Kinetic data will be obtained on the survival of Salmonella in up to 30 selected model food systems (15 cocoa and 15 peanut-based). This data will be obtained using sampling times appropriate for each incubation temperature. Formulated, equilibrated samples will be inoculated and incubated at temperatures of 22, 37, 45, 70, and 80C. Samples at 70 and 80C will be analyzed over a period of 3 days at 7 time points (0, 0.5, 1.0, 3.0, 12, 24, 72 hours). Samples at 37 and 46C will be inoculated and incubated for a period of 8 weeks at 7 time points (0, 1, 3, 7, 14, 21, 35, 56 day). Samples at 22C will be inoculated and incubated for a period of 24 weeks with 8 time points (0, 1, 7, 14, 21, 35, 84, and 168 days). Kinetic data will be replicated three times. Models describing the relationship between lipid hydrophobicity and Salmonella inactivation will be developed based on the previously described data. These models will be used to relate hydrophobicity and water activity of the product to the observed inactivation kinetics. These models will be used validated for their ability to predict Salmonella inactivation in foods including peanut butter, peanut flour, cocoa,and chocolate. Each of these products will have its moisture content adjusted to three levels close to the molecular monolayer. Hydrophobicity will be determined. Inactivation of Salmonella will be determined at 22,50 and 70C. Products will be inoculated as previously described. Salmonella levels will be determined at the end of the selected temperature treatments and compared to the predicted levels.
2012/01 TO 2012/12<br/>
OUTPUTS: Results have been presented at the annual meeting of the UGA Center for Food Safety and the the annual meeting of the International Association for Food Protection.
<br/>PARTICIPANTS: Donald Schaffner, Rutgers University.
<br/>TARGET AUDIENCES: Food industry scientists and regulatory officials engaged in risk assessment.
<br/>PROJECT MODIFICATIONS: Nothing significant to report during this reporting period.
IMPACT: This project is providing food industry scientists with the means to quantitatively predict the degree to which Salmonella will survive in foods of very low water content including peanut butter, chocolate and powder dairy products. The ability to make such predictions will allow for quantitative risk assessment, leading to better decisions in food processing and marketing.