Introducing a novel interaction model structure for the combined effect of temperature and pH on the microbial growth rate

• Published in: International journal of food microbiology Vol. 240; pp. 85 - 96
• Main Authors: Akkermans, Simen, Noriega Fernandez, Estefanía, Logist, Filip, Van Impe, Jan F.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 02-01-2017; Elsevier BV
• Subjects: Bioreactors - microbiology; Cardinal parameter model; Colony Count, Microbial; Computer Simulation; E coli; Environment; Environment models; Environmental conditions; Environmental effects; Escherichia coli K12 - growth & development; Food Microbiology - methods; Food safety; Growth rate; Hydrogen ions; Hydrogen-Ion Concentration; Kinetics; Microbial growth rate; Microorganisms; Models, Biological; pH effects; Prediction models; Predictive microbiology; Stressing; Studies; Temperature; Temperature effects; Microbial growth rate; Predictive microbiology; Cardinal parameter model; Environmental conditions
• ISSN: 0168-1605; 1879-3460
• DOI: 10.1016/j.ijfoodmicro.2016.06.011

Efficient modelling of the microbial growth rate can be performed by combining the effects of individual conditions in a multiplicative way, known as the gamma concept. However, several studies have illustrated that interactions between different effects should be taken into account at stressing environmental conditions to achieve a more accurate description of the growth rate.
In this research, a novel approach for modeling the interactions between the effects of environmental conditions on the microbial growth rate is introduced. As a case study, the effect of temperature and pH on the growth rate of Escherichia coli K12 is modeled, based on a set of computer controlled bioreactor experiments performed under static environmental conditions. The models compared in this case study are the gamma model, the model of Augustin and Carlier (2000), the model of Le Marc et al. (2002) and the novel multiplicative interaction model, developed in this paper. This novel model enables the separate identification of interactions between the effects of two (or more) environmental conditions. The comparison of these models focuses on the accuracy, interpretability and compatibility with efficient modeling approaches. Moreover, for the separate effects of temperature and pH, new cardinal parameter model structures are proposed.
The novel interaction model contributes to a generic modeling approach, resulting in predictive models that are (i) accurate, (ii) easily identifiable with a limited work load, (iii) modular, and (iv) biologically interpretable.