What is Risk Analysis?
Risk analysis is a process for managing risk, which is compromised of three distinct but interactive components: risk assessment, risk management and risk communication.

Risk assessment is a process that is intended to facilitate the description, understanding and management of complex systems (e.g. bacterial spread through the food supply) by providing a framework that allows the evidence and information associated with the issue to be objectively collected and combined to arrive at a conclusion. Risk assessment, in conjunction with risk management and risk communication forms the basis for sound science-based decision-making.

Current projects we are working on:

• Risk Analysis Workshop for the Inter-American Institute for Cooperation on Agriculture (IICA), Summer 2009
• Risk Assessment of Organochlorines in Beef
• Microbial Risk Analysis and Modeling of Microwave Oven Cooking (USDA funded project)

Meet the FPC’s Risk Analysis Group
The FPC’s Risk Analysis Group is a multidisciplinary group of faculty and staff who work together to improve risk based decisions for food safety. Our work draws on diverse disciplines including microbial science, environmental science, engineering, applied mathematics, statistics, and economics. Our staff has published numerous of papers on risk analysis. Some noteworthy examples are as follows:

Dr. Rolando Flores worked on a series of projects where the contamination and distribution of E-coli in ground beef at the different stages of processing were modeled.  Such models help to understand and determine how and at what level pathogenic organisms are transferred and distributed in processing equipment; these organisms can travel through the whole process to the end product, being a major concern to risk analysts. As a result, several articles on the topic were published:

• Flores, R. A. and M. L. Tamplin. 2002. Distribution Patterns of Escherichia coli O157:H7 in Ground Beef Produced by a Laboratory-Scale grinder. Journal of Food Protection 65(12): 1894–1902;
• Flores, R. A. 2004. Distribution of Escherichia coli O157:H7 in beef processed in a table-top bowl-cutter. Journal of Food Protection 67(2): 246-251;
• Flores, R.A. and T. E. Stewart. 2004. Empirical distribution models for Escherichia coli O157:H7 in ground beef produced by a mid-size commercial grinder. Journal of Food Science 69(5):M121-6;
• Flores, R. A., M. L. Tamplin, B. S. Marmer, J. G. Phillips, and P. H. Cooke.  2006. Transfer Coefficient Models for Escherichia coli O157:H7 on Contacts between Beef Tissue and High Density Polyethylene Surfaces. Journal of Food Protection 69(6):1248-1255.

These studies also contributed to the chapter: 

• Flores, R. A. 2006. Modeling the behavior and fate of microbial pathogens in beef processing particle reduction operations. Chapter 15, in Advances in Microbial Foods Safety; ed. by V. Juneja, J. P. Cherry, and M. H. Tunick, ACS Books, ACS: Washington, DC.

Dr. Harshavardhan Thippareddi’s area of expertise is predictive microbiology, development and validation of intervention technologies for control of foodborne pathogens in food systems, and development and implementation of food safety management systems. The early microbial predictive models were developed using model systems (microbiological media). However, these models do not represent the growth of the microorganisms very well. Dr. Thippareddi’s research utilizes the food systems (meat, egg, etc.) to develop the predictive models for the specific foodborne pathogens for use in risk assessment and effectiveness of intervention technologies. Dr. Thippareddi has actively collaborates with a multi-disciplinary, multi-institutional research teams to address food safety issues.  Using those data, he developed dynamic microbial growth models that can predict the microbial population for any temperature profiles such as cooling, heating, etc. Publications related are:

• Gumudavelli, V., J. Subbiah, H. Thippareddi, and P.R. Velugoti. 2007.  Dynamic predictive model for growth of Salmonella Enteritidis in egg yolk.  International Journal of Food Microbiology, 72(7): M254-M262;
• Juneja, V.K., M.V. Melendres, L. Huang, V. Gumudavelli, J. Subbiah, H. Thippareddi. 2007.  Modeling the effect of temperature on growth of Salmonella in chicken.  Food Microbiology, 24: 328-335;
• Wesseling, A., P.R. Velugoti, J. Subbiah,  H. Thippareddi. 2007. Development of a Predictive Model for the Growth of Enterobacter sakazakii in Reconstituted Milk and Soy Infant Formula.  Presented at IFT Annual Meeting, Chicago, IL;
• Bermudez, J., J. Rupnow, J. Subbiah, P.R. Velugoti and H. Thippareddi. 2007. Development of a Predictive Model for the Growth of Escherichia coli O157:H7 in Ground Beef. Presented at IX Congreso Latinoamericano de Microbiología e Higiene de los Alimentos. Margarita Island, Venezuela;
• Jeyamkondan, S., D.S. Jayas, and R.A. Holley. 2001. Microbial growth modelling using artificial neural networks. International Journal of Food Microbiology, 64 (3): 343-354.

Dr. Thippareddi has collaborated with other scientists to integrated heat transfer and microbial predictive models to assess the product safety in case of a process deviation.  The heat transfer models will predict the temperature of product, which will then be fed into the predictive microbial model to predict microbial population.  The regulatory agencies or the processors can assess the microbial safety of the product involved in a process deviation based on these “realistic” predictive models. Examples are his work are:

• Amezquita, C.L. Weller, L. Wang, H. Thippareddi, D.E. Burson, Development of an integrated model for heat transfer and dynamic growth of Clostridium perfringens during the cooling of cooked boneless ham., International Journal of Food Microbiology, 101: 123-144
• Kumar, V., D. Jonnalagadda, J. Subbiah, H. Thippareddi.  2007.  Conjugate Heat Transfer Analysis of an Egg.  Conference Proceedings from Annual Comsol Meeting, Boston, MA;
• Gumudavelli, V., J. Subbiah, H. Thippareddi, L. Wang, and C. Weller.  2007.  Finite Element Modeling of Heat Transfer in Shell Eggs.  Presented at the ASABE Annual Meeting, Minneapolis, MN;
• Gumudavelli, V., J. Subbiah, H. Thippareddi, and C. Weller.  2007.  Development of an Integrated Model of Dynamic Growth of Salmonella Enteritidis and Heat Transfer in Shell Eggs. Presented at the ASABE Annual Meeting, Minneapolis, MN.

Currently, Dr. Thippareddi is working on several research projects with “predictive modeling” core focus to evaluate the safety of the food products and the processes to produce these products safely.
Dr. Jeyamkondan Subbiah area of expertise is predictive microbiology and heat transfer modeling.  One of the key components of a microbial risk assessment model is predictive microbial model that can predict the population dynamics of microorganisms based on time-temperature history.  Dr. Subbiah has collaborated with Dr. Thippareddi (food microbiologist) in collecting data on growth of pathogens in real food systems such as ham, pork, turkey, ground beef, and egg.  Using those data, he developed dynamic microbial growth models that can predict the microbial population for any temperature profiles such as cooling, heating, etc. Publications related are:

• Gumudavelli, V., J. Subbiah, H. Thippareddi, and P.R. Velugoti. 2007.  Dynamic predictive model for growth of Salmonella Enteritidis in egg yolk.  International Journal of Food Microbiology, 72(7): M254-M262;
• Juneja, V.K., M.V. Melendres, L. Huang, V. Gumudavelli, J. Subbiah, H. Thippareddi. 2007.  Modeling the effect of temperature on growth of Salmonella in chicken.  Food Microbiology, 24: 328-335;
• Wesseling, A., P.R. Velugoti, J. Subbiah,  H. Thippareddi. 2007. Development of a Predictive Model for the Growth of Enterobacter sakazakii in Reconstituted Milk and Soy Infant Formula.  Presented at IFT Annual Meeting, Chicago, IL;
• Bermudez, J., J. Rupnow, J. Subbiah, P.R. Velugoti and H. Thippareddi. 2007. Development of a Predictive Model for the Growth of Escherichia coli O157:H7 in Ground Beef. Presented at IX Congreso Latinoamericano de Microbiología e Higiene de los Alimentos. Margarita Island, Venezuela;
• Jeyamkondan, S., D.S. Jayas, and R.A. Holley. 2001. Microbial growth modelling using artificial neural networks. International Journal of Food Microbiology, 64 (3): 343-354.

Dr. Subbiah has also integrated heat transfer and microbial models to assess the risk during process deviation.  The heat transfer models will predict the temperature of product, which will then be fed into the predictive microbial model to predict microbial population.  If there is a process deviation, the microbial risk can be assessed. Examples are his work are:

• Kumar, V., D. Jonnalagadda, J. Subbiah, H. Thippareddi.  2007.  Conjugate Heat Transfer Analysis of an Egg.  Conference Proceedings from Annual Comsol Meeting, Boston, MA;
• Gumudavelli, V., J. Subbiah, H. Thippareddi, L. Wang, and C.Weller.  2007.  Finite Element Modeling of Heat Transfer in Shell Eggs.  Presented at the ASABE Annual Meeting, Minneapolis, MN;
• Gumudavelli, V., J. Subbiah, H. Thippareddi, and C.Weller.  2007.  Development of an Integrated Model of Dynamic Growth of Salmonella Enteritidis and Heat Transfer in Shell Eggs. Presented at the ASABE Annual Meeting, Minneapolis, MN.

Currently, Dr. Subbiah is working on modeling to destruction of pathogens during microwave cooking by integrating the electromagnetic, heat transfer, and microbial inactivation models.  These models will then be used in a risk assessment model for microwave cooking.  The output from the models will be used to develop educational materials, cooking instructions, and packaging design.

Dr. David Jones area of expertise is the development of predictive models.  His modeling has been applied to numerous areas and use modern modeling methodologies such as fuzzy logic and neural networks.Fuzzy logic and neural network modeling is a powerful tool in risk assessment. It can be used to fill gaps in data or predict parameters when few data points are not available. Dr. Jones’s work in the area include:

• Keshwani, Deepak R., David D. Jones, Rhonda M. Brand. 2005. Takagi-Sugeno Fuzzy Modeling of skin permeability. Cutaneous and Ocular Toxicology. (Taylor & Francis) 24:149-163;
• Brown-Brandl, T., D. Jones. 2007. Development and Validation of an Animal Susceptibility Model, ASABE Meeting Presentation; Minneapolis, Minnesota. Paper Number: 074081, June 17-20 where a knowledge-base fuzzy inference system model was developed to predict the susceptibility of an individual animal to heat stress.
• Brown-Brandl, T.M., D.D. Jones, W.E. Woldt. 2005. Evaluating Modeling Techniques for Livestock Heat Stress Prediction, Biosystems Engineering. (Elsevier) 91:513-524, This research look at five types of models were developed to predict cattle heat stress. This information could help the producer manage animal to reduce stress and improve well-being and performance of the animal thus, reducing the risk of losses in production.
• Jones, D., E.M. Barnes. 2000. Fuzzy composite programming to combine remote sensing and crop models for decision support in precision crop management. Agricultural Systems 65(3):137-158 ARD Journal Series No. 12846.

• Hagemeister, M., Jones, D., Woldt, W,E.1993.  Risk assessment and procedure for unregulated landfills using fuzzy composite programming. ASABE Meeting Presentation; Chicago, Illinois. Paper Number: 935502, December 13-17.The tool developed can be used for a variety of users without expertise on landfill hazard assessment.

Dr. Jason Ellis also has an appointment with the University of Nebraska-Lincoln Extension where his focus includes issues management, risk communications, and crisis communications.  Another area of scholarship for Ellis is incorporating communications assessment and evaluation and data-driven communications planning into teaching and Extension. 

• Rhoades, E., & Ellis, J.D. (In press). Food Tube: Coverage of food safety issues through video. Journal of Food Safety
• Ellis, J.D. & Tucker, M. (2009). Factors influencing consumer perception of food hazards. CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources, 4(6), [Online] http://www.cababstractsplus.org/cabreviews
• Sneed, J., Oakley, C.B., & Ellis, J.D. (2005). State Agency Involvement in Food Safety Training for Child Nutrition Programs. Journal of Child Nutrition Management, 31(1), [Online] http://docs.schoolnutrition.org/newsroom/jcnm/06spring/sneed/index.asp
• Ellis, J.D., Strohbehn, C.H., & Henroid Jr., D.H. (2005). Assessing
  on-farm handling practices of Iowa-grown produce and eggs in regard to food safety. Food Protection Trends, 25, 758-761.
• Ellis, J.D., & Henroid Jr., D.H. (2005). A study in Iowa: Teaching food safety in secondary FCS classes. Journal of Family and Consumer Sciences, 97(2), 45-50.
• Ellis, J.D., Sebranek, J.G., & Sneed, J. (2004). Iowa high school students’ perceptions of food safety. Food Protection Trends, 24, 239-245.
• Henroid Jr., D.H., Ellis, J.D., & Huss, J.J. (2003). Methods for answering food safety questions on the World Wide Web. Journal of Applied Communications, 87(4).  http://www.aceweb.org/JAC/v87n4/874-2.htm

To learn more about the Food Processing Center’s risk analysis services, please contact Dr. Flores at 402-472-1664.