Submitted to: International Journal of Food Microbiology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/16/2017
Publication Date: 9/20/2017
Citation: Huang, L. 2017. IPMP Global Fit – A one-step direct data analysis tool for predictive microbiology. International Journal of Food Microbiology. 263:38-48.
Interpretive Summary: Data analysis is critical in predictive microbiology. A new computational tool, USDA Integrated Pathogen Modeling Program-Global Fit (USDA IPMP-Global Fit) is developed to implement a more accurate one-step kinetic analysis method for model development. This tool allows food scientists to directly construct tertiary models for microbial shelf-life prediction and risk assessments in foods.
Technical Abstract: The objective of this work is to describe and introduce the development and performance of a new data analysis tool, the USDA IPMP-Global Fit, for use in predictive microbiology research and development. IPMP-Global Fit is a new software package that is based on a previous product, the USDA-Integrated Pathogen Model Program (USDA-IPMP), but it implements a one-step kinetic analysis approach to directly construct tertiary models by minimizing the global error between the experimental observations and the mathematical models. The USDA IPMP-Global Fit is also equipped with an easy-to-use graphical interface that allows the users to navigate through different stages of data analysis, and provides different combinations of primary and secondary models for analyzing growth and survival curves. The software is tested with different previously published experimental data. The results of data analysis prove that the IPMP-Global Fit is capable of analyzing a variety of growth and survival curves during inverse analysis, and provides accurate estimates of kinetic parameters. In addition, IPMP-Global Fit can be used to improve the experimental design and data collection for more accurate estimation of kinetic parameters. IPMP-Global Fit can be used in combination with the regular USDA-IPMP for solving the inverse problems and developing tertiary models in predictive microbiology.