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<OL> <LI> Develop computer predictive modeling program for growth of foodborne pathogens during heating process deviations <LI>Develop predictive models to quantitatively describe the growth of common foodborne pathogens, Salmonella spp., Clostridium perfringens, and Staphylococcus aureus under constantly varying temperature profiles in a meat (beef, pork) and a poultry meat (turkey) system. <LI>Validate the adequacy of the predictive models under a variety of temperature (fluctuating and non-continuous) profiles simulating thermal (heating) process deviations during cooking of meat products in meat processing establishments. <LI>Develop computer predictive modeling program for growth of C. perfringens during cooling process deviation, with incorporation of antimicrobial ingredients. <LI>Develop predictive models to quantitatively describe the growth of Clostridium perfringens under constantly varying temperature profiles in meat and poultry systems with the incorporation of USDA approved antimicrobial ingredients. <LI> Validate the adequacy of the predictive models under a variety of temperature (fluctuating and non-continuous) profiles simulating thermal (cooling) process deviations during cooling of meat products in meat processing establishments.
NON-TECHNICAL SUMMARY: Growth of foodborne pathogens during processing or storage of meat and poultry products can result in foodborne illness. Current predictive models to describe the growth of these pathogens are inadequate. The purpose of the study is to develop predictive models for growth of foodborne pathogens in meat and poultry products using meat system. These models will be available to the processors to evaluate the risk of foodborne pathogens in their products.
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APPROACH: <BR> OBJECTIVE 1: Three foodborne pathogens of concern, Salmonella spp., Clostridium perfringens, and Staphylococcus aureus were selected for development of models to predict survival and/or growth during heating deviations. (a) Development of Predictive Models: Different strains each of Salmonella spp., S. aureus, and C. perfringens will be utilized in the study. Product will be inoculated in a bio-containment chamber and mixed to attain a uniform inoculum. An injected poultry system with minimal levels of NaCl and phosphate will be used. Subsequent to inoculation, the meat will be placed in the cook-in bags and vacuum packaged. Samples will be homogenized in sterile 0.1% peptone diluent (PW) in a Stomacher Lab Blender for 2 min. Serial dilutions will be prepared in PW and plated on selective media. Several different temperatures within the growth range for each pathogen will be evaluated. Inoculated product will be brought to the target temperature and growth of pathogens will be evaluated. Parameters (lag time and exponential growth rate) will be calculated following methods described by Juneja and Marks (2001). Statistical analysis will be performed using SAS for windows, release 8.0. (b) Validation of the Predictive Models: Bacterial cultures and inoculation, product and microbial enumeration methods described earlier will be followed. Products will be heated and cooled according to the industry practices. Samples will be obtained at frequent intervals based on the predicted generation times from the Tables 5 and 6 obtained from USDA-ARS Pathogen Modeling Program. Growth parameters will be calculated following methods described by Juneja and Marks (2001). Statistical analysis will be performed using SAS for windows, release 8.0. <BR> <BR> OBJECTIVE 2: Clostridium perfringens will be used as an indicator for germination and growth of sporeforming foodborne pathogens. Stabilization guidelines published by USDA-FSIS state that C. perfringens could be used for evaluation of custom stabilization (cooling) parameters employed by the meat industry. (a) Development of Predictive Models: C. perfringens spore coktail (five strain mixture) will be inoculated into meat systems (roast beef, injected pork and injected poultry). The product will be packaged into cook-in bags, vacuum sealed, heat shocked and chilled according to industry practices. Samples will be obtained at specific intervals and C. perfringens populations will be enumerated. Several treatments of meat products containing antimicrobial ingredients such as salts of organic acids will be evaluated. The data will be analyzed by analysis of variance using the General Linear Model procedure of Statistical Analysis System (SAS Institute, Inc., Cary, NC, 1990). Microbial enumeration methods described earlier will be followed. (b) Validation of the Predictive Models: Similar procedures outlined in the earlier section will be utilized, and several different deviation profiles to represent the industry processes will be evaluated for C. perfringens growth and the goodness of fit will be determined.