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Title: SOFTWARE PROGRAMS TO INCREASE THE UTILITY OF PREDICTIVE MICROBIOLOGY INFORMATION

Author
item Tamplin, Mark
item BARANYI, JOZSEF - INST. OF FOOD RESEARCH
item PAOLI, GREG - DECISIONALYSIS RISK CONS.

Submitted to: Modelling Microbial Responses in Food
Publication Type: Book / Chapter
Publication Acceptance Date: 10/3/2003
Publication Date: 10/3/2003
Citation: Tamplin, M.L., Baranyi, J., Paoli, G. 2003. Software programs to increase the utility of predictive microbiology information. IN: Modelling Microbial Responses in Foods. McKellar, R., Lu, X. editors. CRD Press. Chapter 6. p. 233-242.

Interpretive Summary: The advent of computer technology and associated advances in computational power have made it possible to perform complex mathematical calculations that otherwise would be too time-consuming for useful applications in predictive microbiology. Computer software programs provide an interface between the underlying mathematics and the user, allowing model inputs to be entered and estimates to be observed through simplified graphical outputs. An example of model software packages that have gained wide use in the food industry and research communities is the ARS Pathogen Modeling Program. Behind predictive software programs are the raw data upon which the models are built. Access to these data has become important for validating the robustness of models, for bringing transparency to microbial risk assessment, and for advancing modeling techniques. Recent initiatives, such as the relational database, ComBase, developed by the UK Institute of Food Research and the ARS are compiling tens of thousands of predictive microbiology data sets to describe the growth, survival and inactivation of microorganisms, and to accelerate model development and validation. Along with advancements in databases and microbial modeling, a growing need has developed for decision-support tools for navigating across large quantities of data and retrieving specific information. Such information management systems have been used in other scientific fields, but have not been adequately developed and applied to predictive microbiology. Continued development and application of software programs in this field will improve the tools that are available to researchers and risk managers for enhancing the safety and quality of the food supply.

Technical Abstract: The increased use of computer technology in food production, the widespread use of the Internet, and the increasing ease and speed of development of user-friendly software opens up a number of possibilities for software solutions in supporting decisions that are based on predictive microbiology. Software programs have markedly enhanced the use of microbial models by the food industry, risk assessors and food microbiologists. Well designed interfaces with intuitive features allow users to define parameter inputs and easily observe model outputs. The ARS Pathogen Modeling Program is a software package of microbial models that describes growth, survival, inactivation and toxin production under various conditions defined by the user. Model outputs for lag phase duration, generation time and maximum population density are displayed in both graphical and tabular formats. Other commonly used software include the Seafood Spoilage Predictor that estimates microbial spoilage of fisheries products, and curve-fitting software programs, such as Microfit and DMFit. In general, producing software of microbiological data involves databases that systematically categorized and recorded experimental information. A new predictive microbiology initiative called ComBase is aimed at archiving and structuring predictive microbiology data to enhance microbial modeling and risk assessment. The database will be made publicly available via the Internet in 2003. Expert systems can also be tools to formally encapsulate knowledge and data and, given input through a user-interface, generate conclusions and analyses to inform the user. These tools can be used to navigate across vast quantities of predictive microbiology information, retrieve specific information, and then to build and validate microbial models. Furthermore, decision-support tools can potentially direct the user to alternative sources of information when appropriate models and data are lacking. Ultimately, advances in modeling software will provide researchers, students and educators with greater access to information for improving the safety and quality of the food supply.