Introduction
Quantitative microbiological risk assessments (QMRA) in the farm-to-fork continuum heavily rely on mathematical models for growth, survival and inactivation of micro-organisms in different food matrices and processing conditions, collectively subsumed under the heading “predictive microbial models” (PMM). Unfortunately, the currently publicly available PMM are characterized by a great heterogeneity with respect to applicability, quality, validity, documentation, application limits and software requirements.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Michael, R.B., Nicolas, C., Fabian, D., Giuseppe, D.F., Thomas, R.G., Florian, G., Thorsten, M., Peter, O., Christoph, S., Bernd, W.: Knime: The Konstanz Information Miner (2006), http://www.knime.org
R Development Core Team. R: A language and environment for statistical computing (2008), http://www.r-project.org
HyperSQL, http://hsqldb.org
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Weiser, A.A., Filter, M., Falenski, A., Brandt, J., Käsbohrer, A., Appel, B. (2012). An Open-Source Community Resource for Creating, Collecting, Sharing and Applying Predictive Microbial Models (PMM-Lab). In: Aschenbruck, N., Martini, P., Meier, M., Tölle, J. (eds) Future Security. Future Security 2012. Communications in Computer and Information Science, vol 318. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33161-9_65
Download citation
DOI: https://doi.org/10.1007/978-3-642-33161-9_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33160-2
Online ISBN: 978-3-642-33161-9
eBook Packages: Computer ScienceComputer Science (R0)