An official website of the United States government.
LONG-TERM MISSION: To develop improved methods for the design and operation of processing systems for commercially-produced food products, based on the criteria of microbial safety, processing yield, and product quality. <P>OVERARCHING GOALS: (A) Improve the integration of microbial models into engineering models for food handling, storage, processing, and distribution and into risk models, (B) Develop, validate, and disseminate improved methods and tools for validation of food safety processes, (C) Quantify the uncertainty associated with scale-up of microbial models from laboratory studies to pilot- and commercial-scale application, and (D) Develop phenomenological models for microbial inactivation that are based on and linked to basic mechanisms of cell adaptation and inactivation. <P>SPECIFIC OBJECTIVES (low-moisture foods): (1) To develop effective mathematical models for quantifying the effect of product water activity on the inactivation rate of Salmonella in/on multiple low-moisture food products (e.g., nuts, flour, and pastes) subjected to pathogen reduction processes (e.g., fluid-based heating, chemical sanitation, and irradiation), (2) To validate the new models via inoculated, pilot-scale challenge studies with Salmonella on representative products subjected to the various processes in a unique, Biosafety Level-2 pilot processing facility, (3) To integrate microbial survival and inactivation models into coupled heat and mass transfer models for drying, storage, and heating of low-moisture food products, (4) To develop an academic/industry/government consortium focused on strategies for commercial implementation and validation of improved methods for pasteurization of low-moisture food products. <P>SPECIFIC OBJECTIVES (meat and poultry): (5) To modify our previously developed, path-dependent model for the effects of sub-lethal thermal history on the subsequent thermal inactivation rate of Salmonella in meat and poultry products, to reflect cellular mechanisms, (6) To develop, validate on the pilot-scale, and distribute a universal model for thermal inactivation of Salmonella in meat and poultry products, as a function of species, composition, and structure, (7) To conduct a meta-analysis of published microbial inactivation data, or order to quantify the various sources of uncertainty inherent in inactivation models, (8) To develop and validate novel models for the rate of bacterial transfer between meat and poultry products and contact surfaces, (9) To integrate a mechanistic model for bacterial transport in postmortem muscle tissue with thermal inactivation models to quantify the relative risk of pathogen survival in intact and non-intact whole muscle meat products.<P> SPECIFIC OBJECTIVES (fresh produce): (10) To develop and validate inactivation models for microbial disinfection/inactivation processes applied to fresh produce, (11) To develop and validate a novel model for rate of bacterial transfer between fresh produce, equipment surfaces, and processing media, and (12) To develop a decision tool for technology-neutral assessments of the suitability of irradiation as a food safety intervention for various fresh produce products.
Non-Technical Summary: This project focuses on the development of engineering solutions to critical food safety challenges facing the U.S. food industry. The need for such research is motivated by: (1) on-going regulatory changes that increasingly are putting the burden of proof for product safety on the industry, (2) the emergence of low-moisture foods (e.g., nuts, peanut butter, grain products) as vehicles for major outbreaks and/or recalls associated with Salmonella and Escherichia coli O157:H7, (3) the growth of ready-to-eat (RTE) food product categories, particularly in meat and poultry, and (4) the insufficiency of data and tools for the industry to reliably validate and optimize processing interventions against key pathogens in critical food sectors, particularly low-moisture foods and meat and poultry products. To help address these key challenges, we will work at the interdisciplinary interface between predictive food microbiology and food process engineering, in order to optimize the utility of the resulting data and tools. Key food products (e.g., nuts, peanut butter, grain products, meat and poultry products, and fresh produce) will be inoculated with the pathogens of concern and subjected to a variety of lethal processes. The resulting data will be used to build mathematical models for the relationships between the process conditions, the product characteristics, and the resulting reduction in pathogen population. These models will be validated by inoculating actual food products with the target pathogen and processing it in a unique Biosafety Level-2 Pilot Processing Facility at Michigan State University, in equipment that approximates industrial processes. The result will be improved methods and tools for designing, operating, and validating food processing systems, with respect to microbial safety. <P> Approach: OVERALL: Although the individual objectives and tasks in this project will be, to some degree, concurrent and iterative, rather than sequential, the overall/generalized progression of activity can still be described as follows: (1) Conduct laboratory-scale studies to generate microbiological and physicochemical data for the various pathogens, products, and processes described in the specific objectives, (2) Develop principle-based and empirical mathematical models to describe those changes, and use the laboratory data to estimate the parameters of those models, (3) Conduct pilot-scale studies to validate the models under commercially relevant processing conditions, and (4) Utilize the validated models to develop and deploy tools directly applicable to commercial applications. PRODUCTS: Certain high priority products will be utilized as representative cases for the various product categories. Each of three key categories of low-moisture foods will be represented by one product previously linked to salmonellosis outbreaks: (1) large particulates - almonds, (2) powders - wheat flour, and (3) pastes - peanut butter. For work with meat and poultry products, turkey, pork, and beef will be acquired from the MSU meat plant (or from controlled, industrial sources) and used for the experimental portions of the project, either as intact, boneless products, or as ground and formed products from the same original lots of product. For the fresh produce work, primarily associated with the x-ray irradiation objectives, iceberg lettuce, baby spinach, jalapeno peppers, and blueberries will be representative of leafy greens, fresh vegetables, and fresh berries. PATHOGENS: Most of the objectives focus on Salmonella. Serovars from outbreaks linked to low moisture products and meat and poultry products are already maintained I the PIs laboratory, and will be used for the corresponding objectives. Additionally an E. coli O157:H7 cocktail (already in our group) will be used for Objective 9. PROCESSES: Inoculated samples will be subjected to a variety of lethal treatments (e..g., heat, x-ray irradiation), depending on the specific objective. For the thermal treatments (low-moisture products and meat and poultry products), heating will be accomplished via both a custom laboratory-scale oven and via two pilot-scale oven systems in the MSU Biosafety Level-2 Pilot Processing Facility, which can approximate commercial processes in moist-air convection/roasting and moist-air impingement ovens (i.e., a range of temperatures, humidities, and air velocities). For the irradiation treatment, samples will be exposed to low-energy (70 kVp) x-ray irradiation in the pilot-scale irradiator (Rayfresh Foods, Ann Arbor, MI), currently housed in the MSU BSL-2 Pilot Processing Facility. ANALYSES: All of the above are aimed at generating data for the development or validation of mathematical models for microbial survival, inactivation, and/or transport. Model parameters will be estimated primarily through non-linear regression, and model performance will be quantified through multiple measures, including the root mean squared error and the Akaike Information Criterion.