Submitted to: International Journal of Food Microbiology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/13/2013
Publication Date: 2/3/2014
Publication URL: http://handle.nal.usda.gov/10113/58480
Citation: Huang, L. 2014. IPMP 2013 - A comprehensive data analysis tool for predictive microbiology. International Journal of Food Microbiology. 171(2014)100-107. Interpretive Summary: Mathematical models are increasingly used in microbial shelf-life prediction and risk assessments of foods. However, model development is a sophisticated process that usually requires advanced learning and skills in mathematics, statistics, and computer programming. The objective of this work was to develop an easy-to-use, comprehensive data analysis and model development tool for predictive microbiology research. This tool allows anyone, with a basic knowledge in predictive microbiology, to develop mathematical models for shelf-life prediction and risk assessments of foods. It can also be used in higher education settings to teach students about model development in predictive microbiology.
Technical Abstract: Predictive microbiology is an area of applied research in food science that uses mathematical models to predict the changes in the population of pathogenic or spoilage microorganisms in foods undergoing complex environmental changes during processing, transportation, distribution, and storage. It finds applications in shelf-life prediction and risk assessments of foods. The objective of this research was to evaluate the performance of a new user-friendly comprehensive data analysis and model development tool, the Integrated Pathogen Modeling Model (IPMP 2013), recently developed by the USDA Agricultural Research Service. This tool allows users, without programming, to analyze experimental kinetic data and develop models for predictive microbiology. Data curves previously published in literature were used to test the models in IPMP 2013. The accuracies of the data analysis and models derived from IPMP 2013 were compared in parallel to commercial or open-source statistical packages, such as SAS® or R. Several models were analyzed and compared, including a three-parameter logistic model for growth curves without lag phases, reduced Huang and Baranyi models for growth curves without stationary phases, growth models for complete growth curves (Huang, Baranyi, and re-parameterized Gompertz models), survival models (linear, re-parameterized Gompertz, and Weibull models), and secondary models (Ratkowsky Square-root, Huang Square-root, Cardinal, and Arrhenius-type models). The comparitive analysis showed that the results from IPMP 2013 were equivalent to those obtained from SAS® or R. This work suggested that the IPMP 2013 can be used as a free alternative to SAS®, R, or other more sophisticated statistical packages for model development in predictive microbiology.