2009 Annual Report
1a.Objectives (from AD-416)
To evaluate, validate, and where necessary, develop new innovative, robust and valid predictive models for the responses of microbial pathogens, including foodborne threat agents, in select food matrices, as a function of: temperature, food formulation, competitive microflora, physiological history, and surface transfer.
To develop novel approaches to assess model performance and robustness, leading to more efficient strategies for producing and extrapolating models to different classes of food.
To determine the probability distribution of lag phase duration (LPD) for foodborne pathogens, as a function of the previous bacterial physiological history, to allow risk managers to estimate worst-and best-case scenarios for pathogen behavior, depending on likely sources of contamination; To identify molecular markers that discriminate bacterial lag, growth and stationary phases, thus leading to more mechanistic models and greater certainty for LPD prediction.
1b.Approach (from AD-416)
Quantitative data will be collected for the effects of selected environmental parameters on foodborne pathogen growth, survival and inactivation. Relevant environmental conditions will include food formulation, native microbial flora, inoculum level, bacterial history, and the effects of food process operations. Priority pathogen-food combinations will be identified through stakeholder interactions and by identifying sensitive data gaps in microbial risk assessment. Experimental data will be used to confirm and where necessary produce primary growth and inactivation models, as well as probabilistic models for growth/no growth interfaces and microbial transfer among food processing surfaces. Model performance will be described using independent validation data from ongoing experiments with food matrices and microbiology databases such as ComBase. The resulting technologies will be transferred to stakeholders vis the ARS Pathogen Modeling Program and process risk model software.
A series of experiments were being conducted to determine the influence of nitrite (0 to 200 ppm), NaCl (0 to 6%), and sodium pyrophosphate (0 to 0.5%) levels on the germination and outgrowth of Clostridium perfringens spores during cooling of cooked beef and poultry products. Finally, predictive model for growth of Clostridium perfringens during cooling of cooked beef and poultry products based on these product composition factors are being developed. The growth data /predictive models on the safe cooling rate of meat will enable the food industry to assure that cooked products remain pathogen-free.
A series of experiments were conducted to investigate and model the growth and survival of Salmonella on chicken skin as a function of strain variation, initial dose and previous history. Salmonella serotype Kentucky was found to grow slower than serotypes Typhimurium and Hadar, which had similar growth. Survival and growth of Salmonella was found to differ as a function of initial dose. Freezing of chicken skin for one week did not alter the subsequent growth and survival of Salmonella. A general regression neural network model for survival and growth of Salmonella on chicken skin as a function of time, temperature and serotype was developed.
Experiments were conducted to determine the impact of prior history on the lag phase duration of L. monocytogenes and Escherichia coli O157:H7. The pathogens were exposed to 25 - 75ppm chlorine for 1 h prior to assessing the lag phase of 3 – 4 log CFU/g cells on sliced ham at temperatures ranging from 4 – 24C. While lag phase of L. monocytogenes varied considerably, E. coli O157:H7 lag phase was longer after exposure to higher chlorine concentration and at lower incubation temperatures.
The fate of RpoS, an alternate sigma factor responsible for regulating many of the genes responsible for survival of bacteria, in the transition from stationary phase to lag phase growth using Escherichia coli O157:H7 as a model strain was studied. To verify that E. coli O157:H7 (EDL933 and Sakai strains) expressed a full length version of RpoS, the DNA sequence of this gene in EDL933 and Sakai strains has been initiated and the sequence data are being generated.
Predictive model for growth of L. monocytogenes in ready-to-eat (RTE): L. monocytogenes is a pathogen commonly associated with foodborne illness caused by consumption of contaminated ready-to-eat meat. Models were developed to describe the survival and growth of L. monocytogenes and native microflora in ready-to-eat ham. The models will help the manufacturers to determine the effect of temperature during distribution and storage of ham on the safety and quality of their products.
Predictive model for transfer of Salmonella during mechanical slicing of deli meats: Food safety managers currently lack the ability to predict the microbial pathogen transfer in slicing operations for ready-to-eat foods. Models were developed for pathogen transfer prediction during mechanical slicing for RTE deli meats. Predictive models will be useful in developing HACCP plans and in risk assessment development for ready-to-eat meats. By understanding the surface transfer, the production or retail (Franchise) equipment and operations for RTE deli meat may be further improved and therefore, reduce the possibility of outbreaks.
Surface shear influences the foodborne pathogens transfer during mechanical slicing of deli meats: To predict pathogen transfer in slicing operations for RTE foods, understanding the impact of surface shear on pathogens is needed. The mechanical surface shear stress was found to kill a large amount (99%) of L. monocytogenes, as demonstrated by confocal microscopy. These findings will contribute to the development and verification of microbial slicing surface transfer models for RTE meats.
Predictive model for growth of Salmonella on chicken skin: Salmonella spp. are a leading cause of illness in the United States by the fecal-oral route. Chicken is a leading source of Salmonella illness in humans because the skin, which is exposed to feces on-the-farm, is often sold with the product. Using state-of-the-art methods to investigate and model the behavior of Salmonella on chicken skin, the first computer model for predicting the risk of Salmonella illness from chicken with skin was developed and validated. The model provides better predictions than previous models developed in laboratory media and will not only enhance chicken safety but will save a chicken processing plant up to $400,000 a day by avoiding condemnations of ‘safe-chicken’ due to more accurate predictions than previous models.
Virulent plasmid stability during growth of Yersinia pestis in ground beef: To fully assess the potential risk of illness, the stability of the virulence plasmid in Y. pestis during its growth in raw ground beef needs to be determined. The virulence plasmid was retained in Y. pestis in raw ground beef at all levels of fat when stored at refrigerator temperatures and during its growth at 10-30C. This suggests that raw ground beef contaminated with virulent Y. pestis could cause oropharyngeal plague if there was a refrigeration failure leading to temperature (10-25C) abuse, or if the meat was not properly cooked. The resultant disease may lead to outbreaks of highly infectious pneumonic plague.
Thermal death time model accurately determines the thermal intervention for Salmonella in beef: Defined the heat treatment (55-73.9C) required to achieve a specified lethality for Salmonella in ground beef. The thermal death predictive models for the pathogen, which can predict D-values for any combinations of the factors that are within the range of those tested, was developed. Using these inactivation kinetics or predictive model for Salmonella, food processors can design thermal processes for the production of a safe beef product with extended shelf life.
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Hwang, C., Porto Fett, A.C., Juneja, V.K., Ingham, S., Ingham, B., Luchansky, J.B. 2009. Modeling the survival of escherichia coli o157:h7, listeria monocytogenes and salmonella typhimurium during fermentation, drying, and storage of soudjouk-style feremented sausage. International Journal of Food Microbiology. 129:244-252.
Juneja, V.K., Marks, H., Thippareddi, H. 2009. Predictive model for growth of Clostridium perfringens during cooling of cooked ground chicken. Innovative Food Science and Emerging Technologies. 10:260-266.
Juneja, V.K., Melendes, M., Huang, L., Subbiah, J., Thippareddi, H. 2009. Mathematical modeling of growth of Salmonella in raw ground beef under isothermal conditions from 10 to 45 Degree C. International Journal of Food Microbiology. 131:106-111.
Juneja, V.K., Bari, M.L., Inatsu, S., Kawamoto, S., Friedman, M. 2009. Thermal Destruction of Escherichia coli O157:H7 in Sous-vide Cooked Ground Beef as affected by Tea Leaf and Apple Skin Powders. Journal of Food Protection. 72(4):860-865.
Bhaduri, S., Wesley, I.V., Richards, H., Draughon, A., Wallace, M. 2009. Clonality and Antibiotic Susceptibility of Yersinia enterocolitica Isolated From U.S. Market Weight Hogs. Foodborne Pathogens and Disease. 6(3):351-356.
Bhaduri, S., O Connor, C. 2008. Comparison of Virulence Plasmid (pYV/pCD)-Associated Phenotypes in Yersinia Species. Journal of Food Safety. 28:453-466.
Wesley, I.V., Bhaduri, S., Bush, E. 2008. Prevalence of Yersinia enterocolitica in market weight hogs in the United States. Journal of Food Protection. 71(6):1162-1168.
Juneja, V.K., Friedman, M. 2008. Carvacrol and Cinnamaldehyde Facilitate Thermal Destruction of Escherichia coli O157:H7 in Raw Ground Beef. Journal of Food Protection. 71(8):1604-1611.
Oscar, T.P. 2009. PREDICTIVE MODEL FOR SURVIVAL AND GROWTH OF SALMONELLA TYPHIMURIUM DT104 ON CHICKEN SKIN DURING TEMPERATURE ABUSE. Journal of Food Protection. 72(2):304-314.
Hwang, C. 2009. THE PROBABILITY OF GROWTH OF LISTERIA MONOCYTOGENES IN SIMULATED SMOKED SALMON AND TYPTIC SOY BROTH AS AFFECTED BY SALT, SMOKE COMPOUND AND STORAGE TEMPERATURES. International Journal of Food Microbiology. 129:244-252.
Okahisa, N., Inatsu, Y., Juneja, V.K., Kawamoto, S. 2008. Evaluation and control of the risk of food borne pathogens and spoilage bacteria present in “Awa-Uirou”, a sticky rice cake containing sweet red bean paste. Foodborne Pathogens and Disease. 5(3):351-359.