A Generalized Phenomenological Model for Bacterial Transfer to/from Fresh Produce

Non-Technical Summary:<br/>
There is a growing body of literature addressing bacterial transfer to/from food products and contact surfaces; however, it is almost exclusively empirical, resulting in transfer models that are purely probabilistic or empirical curve fitting, unconnected to the underlying governing mechanisms. Although adhesion/transfer processes are organism- and substrate-specific, general principles must govern the observed phenomena. Therefore, there is a need for a unifying modeling framework that bridges micro- and macro-scale knowledge in this domain. The overall goal of this project is to develop a conceptual framework for modeling bacterial adhesion and transfer to/from food products, which will enable improvements in future experimental studies, process models, and risk assessments.
<br/>The specific objectives are to: (1) Aggregate existing bacterial transfer data associated with foodborne pathogens across a range of food products; (2) Conduct a meta-analysis of those data to elucidate the relative importance of various factors affecting attachment/transfer; (3) Formulate and test a generalized phenomenological model for bacterial attachment/transfer among multiple components of a food processing environment; and (4) Utilize that model to theoretically analyze the relative impact of multiple bio/chem/physical factors on the overall attachment/transfer process. This project will build a theoretical bridge across the existing gap between the body of previous, basic research on bacterial adhesion and the growing body of empirical work focused on high priority foodborne pathogens on "at risk" foods. Such a bridge will significantly increase the probability of basic research impacting real practices and of future applied work being based on a consistent and phenomenologically sound framework.
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Approach:<br/>
The overall approach entails aggregation of previously disconnected bacterial transfer data from prior studies (Obj 1), a meta-analysis of those data to elucidate the relative strength of critical bio/chem/physical factors (Obj 2), formulation of a mathematical model that is consistent with the physicochemical mechanisms controlling the observed transfer behaviors (Obj 3), and utilization of that model for a sensitivity analysis to quantify the relative impact of multiple attachment mechanisms on the overall transfer process (Obj 4). We will bring the transfer data into the database by three methods: (1) Aggregation of the significant transfer data that have been generated at MSU over the past decade, as presented in various graduate theses and journal articles; (2) Contacting colleagues/peers who have published the existing transfer data, to ask for their direct contribution of the original data; (3) Direct acquisition of the data from other theses and publications, where the original (raw) data are reported in table form; or (4) Extraction of numerical data from graphical presentation in publication (the least desirable method). As the database is constructed and populated, we will employ multiple meta-analysis techniques to: (a) characterize the inherent properties of the aggregated data and (b) extract novel information that can be constructed only by aggregating multiple data sets, not by single studies. We then will pursue two parallel approaches to model development, in order to evaluate which of the two best links the fundamentals of the transfer process to the observed data: (1) dimensional analysis, and (2) mechanics-based.
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Progress:<br/>
2012/01 TO 2012/12<br/>
OUTPUTS: This is the first annual report for this project. As planned, the first year entailed tasks focused on objectives 1 and 3, and the beginning of objective 2. Specifically (obj 1), the structure for a bacterial transfer database has been created, and an initial population of data sets has been inserted into the database. The initial stage of data set acquisition from other research teams is underway. To date, six studies containing 75 data sets, 253 transfer curves, and 5,838 data points encompass the major four ready-to-eat (RTE) meat products (ham, turkey, salami, bologna), equipment surfaces (mechanical slicers, kitchen knives, cutting boards, conveyor belts, and countertops), and contact event types (sequential static surface contacts, single knife slicing, and mechanical delicatessen slicing). Three models (linear, Weibull, and two-phase Weibull-linear with a critical contact value) were fit to each of the transfer curves (obj 2); the most likely models were determined for each transfer scenario and food type using the Akaike Information Criterion (AIC) and root mean squared error (RMSE). Once the database is expanded, a broader meta-analysis of transfer response characteristics (obj 2) will be possible, and will be a focus in years 2 and 3 of the project. Additionally (obj 3), an initial bacterial transfer model has been developed using dimensional analysis methods. The Buckingham Pi Theorem was applied to formulate a generalized model for bacterial transfer occurring between fresh produce and wash/conveying water or equipment contact surfaces. Initially, 11 candidate variables (product and process) were identified for equipment contact events (slicing, shredding, and conveying), and 21 were identified for water washing/conveying. Based on expert knowledge, variables unlikely to significantly affect transfer were excluded, to yield 6 and 9 variables for equipment contact events and water washing/conveying processes, respectively. Application of the Buckingham Pi Theorem accounted for the fundamental units of each variable and the total number of variables in each process to reduce the model to a smaller number of dimensionless (Pi) terms. Currently, bench-scale transfer tests (obj 3) are being constructed, and will be a focus in the second year of the project.
<br/>PARTICIPANTS: The following are the individuals who worked on this project during the reporting period, and a brief description of their roles on the project: Bradley Marks (PI) - managing overall experimental plans, data management and analysis strategies, student supervision, and project team meetings and progress. Elliot Ryser (co-PI) - supervising one graduate student and experimental microbiology work. Amanda Benoit (MS student in biosystems engineering) - collection of prior bacterial transfer data and initial meta-analysis of transfer curve characteristics. Beatriz Mazon (PhD student in biosystems engineering) - developing generalized bacterial transfer model and designing bench-scale transfer experiments. Lin Ren (MS student in food science) - conducting bench- and pilot-scale bacterial transfer experiments with fresh produce.
<br/>TARGET AUDIENCES: The principal target audience for this project is other researchers conducting/planning bacterial transfer studies, or those utilizing the results of such studies for risk analyses. The results of this project will build a theoretical bridge across the existing gap between basic research on bacterial adhesion and the growing body of empirical work focused on high priority foodborne pathogens for "at risk" foods (e.g., fresh produce). Such a bridge will significantly increase the probability of basic research impacting real practices and of future applied work being based on a consistent and phenomenologically sound framework.
<br/>PROJECT MODIFICATIONS: Nothing significant to report during this reporting period.
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IMPACT: The preliminary aggregate analysis of transfer data for Listeria monocytogenes and ready-to-eat meat products (obj 2) revealed that the Weibull model was the best choice (based on AIC) for 8/8, 14/37, and 27/38 of the knife, slicer, and static contact data sets, respectively. For turkey, ham, and salami slicing data, the two-phase model yielded a mean critical contact value of ~9, ~9, and ~2, indicating fundamental differences among transfer responses. In contrast, sequential static contact data yielded the same shaped response regardless of the meat type or surface. Aggregating data from multiple studies revealed underlying transfer characteristics that were not previously evident or reported in the individual studies. However, there remains a need for standard methods or reporting expectations, in order to maximize the future utility of transfer studies. The preliminary generalized transfer model (obj 3), based on dimensional analysis methods, included two and five dimensionless (Pi) terms, respectively, for the equipment contact and water wash/conveying processes. Each dimensionless term in the resulting models (like, for example, a Reynolds number in fluid flow) can be applied to determine relative impact of key variables on transfer. For example, one Pi term relates friction force at the surface, contact time, and the initial bacterial population on the donor surface to resulting transfer. For the water transfer events, the Pi terms relate water velocity and product dimensions, which can illustrate the general dependency of transfer on fluid shear. This novel approach to modeling bacterial transfer will enable optimized designs of future transfer experiments, in order to yield data that improve the utility of the generalized transfer model for process improvement and risk modeling.