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The USDA Integrated Pathogen Modeling Program - Global Fit (IPMP - Global Fit)
What is IPMP - Global Fit?
IPMP-Global Fit is an extension of the USDA Integrated Pathogen Modeling Program (IPMP). It is designed as a one-step direct kinetic analysis tool that constructs a tertiary model from the entire experimental data set of growth and inactivation. IPMP-Global Fit tries to analyze and fit the entire experimental data for both primary and secondary models and obtain the kinetic parameters that minimize the global error of the primary model. It differs from IPMP, which is design to analyze individual growth or inactivation curves. IPMP-Global Fit will analyze all isothermal growth or inactivation curves obtained under different conditions together, and try to derive kinetic parameters that are optimized for the entire data set. It is a tool developed to implement the one-step kinetic analysis methodology proposed by Huang (2015a, 2015b, and 2016).
What is required to use IPMP - Global Fit?
IPMP-Global Fit can be run under Microsoft Operating Systems (32 or 64 bit).
What models are included in IPMP - Global Fit?
IPMP-Global Fit includes four primary models for growth and survival and six secondary models. It will be expanded gradually to include more models.
Tutorial
Click for tutorial (link to IPMP Global Fit Tutorial.pdf).
Download IPMP - Global Fit
IPMP-Global Fit has been compiled for the Windows operating system. It is distributed as a zip file under a directory (IPMPGlobalFit). Please download the zip file and unzip (copy) it to a directory (IPMPGlobalFit). Please keep every file under the same directory. For detailed installation instructions, please click (link to Installation instructions.pdf).
Link to download IPMP-Global Fit (IPMPGlobalFit 1-5-17.zip).
For technical questions and comments, please contact lihan.huang@ars.usda.gov
DISCLAIMER AND ASSUMPTION OF RISK
The USDA Integrated Pathogen Modeling Program-Global Fit (IPMP-Global Fit) is a software tool developed by the USDA Agricultural Research Service (ARS) for data analysis and model development in predictive microbiology. USDA grants to each recipient of this software non-exclusive, royalty free, world-wide, permission to use, copy, publish, distribute, perform publicly and display publicly this software. We would appreciate acknowledgement if the software is used.
The software is provided “as is”, without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose, noninfringement and any warranty that this software is free from defects. In no event shall USDA be liable for any claim, loss, damages or other liability, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the software or the use or other dealings in the software.
The risk of any and all loss, damage, or unsatisfactory performance of this software rests with you, the recipient. USDA provides no warranties, either express or implied, regarding the appropriateness of the use, output, or results of the use of the software in terms of its correctness, accuracy, reliability, being current or otherwise. USDA has no obligation to correct errors, make changes, support this software, distribute updates, or provide notification of any error or defect, known or unknown. If you, the recipient, rely upon this software, you do so at your own risk and you assume the responsibility for the results. Should this software prove defective, you assume the cost of all losses, including but not limited to, any necessary servicing, repair or correction of any property involved.
Please contact Dr. Lihan Huang (Lihan.Huang@ars.usda.gov ) for technical questions.
References
Huang, L. 2015a. Growth of Staphylococcus aureus in cooked potato and potato salad - a one-step kinetic analysis. J. Food Science, 80(12): M2837-M2844.
Huang, L. 2015b. Direct construction of predictive models for describing growth of Salmonella Enteritidis in liquid eggs - a one-step approach. Food Control, 57: 76-81. 2015.
Huang, L. 2016. Mathematical modeling and validation of growth of Salmonella Enteritidis and background microorganisms in potato salad – one-step kinetic analysis and model development. Food Control, 68: 69-76. 2016.
