Alternative Approach To Modeling Bacterial Lag Time, Using Logistic Regression as a Function of Time, Temperature, pH, and Sodium Chloride Concentration

Abstract

<jats:title>ABSTRACT</jats:title><jats:p>The objective of this study was to develop a probabilistic model to predict the end of lag time (λ) during the growth of<jats:named-content content-type="genus-species">Bacillus cereus</jats:named-content>vegetative cells as a function of temperature, pH, and salt concentration using logistic regression. The developed λ model was subsequently combined with a logistic differential equation to simulate bacterial numbers over time. To develop a novel model for λ, we determined whether bacterial growth had begun, i.e., whether λ had ended, at each time point during the growth kinetics. The growth of<jats:named-content content-type="genus-species">B. cereus</jats:named-content>was evaluated by optical density (OD) measurements in culture media for various pHs (5.5 ∼ 7.0) and salt concentrations (0.5 ∼ 2.0%) at static temperatures (10 ∼ 20°C). The probability of the end of λ was modeled using dichotomous judgments obtained at each OD measurement point concerning whether a significant increase had been observed. The probability of the end of λ was described as a function of time, temperature, pH, and salt concentration and showed a high goodness of fit. The λ model was validated with independent data sets of<jats:named-content content-type="genus-species">B. cereus</jats:named-content>growth in culture media and foods, indicating acceptable performance. Furthermore, the λ model, in combination with a logistic differential equation, enabled a simulation of the population of<jats:named-content content-type="genus-species">B. cereus</jats:named-content>in various foods over time at static and/or fluctuating temperatures with high accuracy. Thus, this newly developed modeling procedure enables the description of λ using observable environmental parameters without any conceptual assumptions and the simulation of bacterial numbers over time with the use of a logistic differential equation.</jats:p>

Prediction of spoilage of tropical shrimp (Penaeus notialis) under dynamic temperature regimes

“…It has been shown that the maximum specific growth rate (μ max ) is reciprocally proportional to the lag time (λ) (Baranyi and Roberts, 1994;Koutsoumanis et al, 2006;McMeekin et al, 1993;Zwietering et al, 1994a). Although the product μ max × λ is not always constant (Koseki and Nonaka, 2012), it generally ranges between approximately 0 and 4 (Koutsoumanis et al, 2006;Zwietering et al, 1994a). In the present study, the lag time was estimated assuming that μ max × λ = 1 and μ max × λ = 4.…”

Quantitative phenotypic analysis of multistress response inZygosaccharomyces rouxiicomplex

“…Our results indicate that the quantification of k is more influenced by possibly uncommon shapes, induced by high stressful conditions, than the other parameters l, A and AUC, resulting both in negative values and broad CI. Modelling of bacterial lag time is complicated because the mechanisms governing lag time are not fully understood (Swinnen et al, 2004;Kosekia & Nonaka, 2012). It is known that bacterial lag time is influenced not only by current environmental conditions but also by multiple other factors, such as previous growth conditions (Swinnen et al, 2004;Ryall et al, 2012), initial cell counts (Baranyi & Pin, 1999) and stochastic variability (McKellar & Hawke, 2006;Koutsoumanis, 2008;Ginovart et al, 2011).…”

Predictive Microbiology Coupled with Gas (O2/CO2) Transfer in Food/Packaging Systems: How to Develop an Efficient Decision Support Tool for Food Packaging Dimensioning

“…Sometimes, the effect of changing conditions could be predicted, such as the impact of transient temperature (ComBase [http://www.combase.cc], Sym’Previus [http://www.symprevius.net]), but, generally, only constant parameters are taken into account. If the environmental conditions and their impacts are taken into account on the μ max (using, e.g., the cardinal values), these are generally neglected on the lag phase in these kinds of models (Koseki and Nonaka ).…”