2 Primary growth models

The actual version includes primary growth models that describe microbial concentration as a function of time at constant environmental conditions. The model inputs are:

$t$: time, assuming time zero as the beginning of the experiment; and
$Y_{(t)}$: the natural logarithm of the microbial concentration $X_{(t)}$ measured at time $t$.

Users should make sure that the microbial concentration input is entered in natural logarithm, $Y_{(t)} = ln(X_{(t)})$.

The number of model parameters is dependent upon the completeness of the microbial growth curve. The following parameters can be estimated using this web application:

$Y_0$: the natural logarithm of the initial microbial concentration at $t=0$;
$\mu_{max}$: maximum specific growth rate given in time $units^{-1}$;
$\lambda$: duration of the lag phase in time units; and
$Y_{max}$: the natural logarithm of the maximum concentration reached by the microorganism.

A full model should be adjusted to a complete microbial curve, where the lag phase, exponential phase and stationary phase can be identified. The predmicror can also fit reduced models. A no-stationary phase model is to be adjusted to an experimental microbial curve that presents lag phase and exponential phase, whereas a no-lag phase model should be adjusted to an experimental curve composed of exponential phase and stationary phase. An experimental growth curve that presents only exponential phase cannot be analysed using the predmicror functions.

2.1 Full growth models

predmicror can adjust four nonlinear models to complete microbial growth curves: Huang model, Rosso model, Baranyi & Roberts model and the Zwietering reparameterised Gompertz model.

2.1.1 Huang model

The Huang growth model was developed by Huang (2008).

\[Y_{(t)} = Y_0 + Y_{max} -log \left( e^{Y_0} + (e^{Y_{max}} - e^{Y_0}) \times e^{-\mu_{max} \times B_{(t)}} \right)\]

\[B_{(t)} = t + \frac{1}{\alpha} \times log \left( \frac{1 + e^{-\alpha \times (t-\lambda)} }{1 + e^{\alpha\times \lambda}} \right)\]

After evaluating multiple growth data sets, Huang (2013) recommended fixing the parameter $\alpha$ to 4.0, thus predmicror considers $\alpha=4.0$.

2.1.2 Rosso model

The Rosso growth model is a simple two-phase model proposed by Rosso et al. (1996).

\[ Y_{(t)} = \begin{cases} Y_0 & \text{if t $\leq$ $\lambda$}\\ Y_{max}-log \left[1 +\left( \frac{e^{Y_{max}}}{e^{Y_0}}-1 \right) e^{-\mu_{max} (t-\lambda)} \right] & \text{if t > $\lambda$} \end{cases} \]

2.1.3 Baranyi & Roberts model

The original Baranyi & Roberts model attributes the lag phase to the need to synthesise an unknown substrate q that is critical for growth, whose initial value $q_0$ is a measure of the initial physiological state of the microbial cells (Baranyi & Roberts (1994)).

predmicror implements the Baranyi & Roberts model with basis on the transformation $h_0 = \mu_{max} \times \lambda$, in order to estimate $\lambda$. Thus, the model parameterisation used is:

\[Y_{(t)} = Y_0 + \mu_{max} \times A_{(t)} - \frac{1}{m} \times log \left[ 1 + \frac{exp(m \times \mu_{max} \times A_{(t)})-1}{exp(m \times (Y_{max}-Y_0)) } \right]\]

\[A_{(t)} = t + \frac{ log \left[ exp(-\mu_{max} \times t) + exp(-\mu_{max} \times \lambda) - exp(-\mu_{max} \times t-\mu_{max} \times \lambda) \right] }{\mu_{max}}\]

Most of the times, the parameter m, which characterises the curvature before the stationary phase is assumed to be 1.0. The predmicror simplifies this model by assuming $m=1.0$.

2.1.4 Zwietering reparameterised Gompertz model

predmicror adjusts the Gompertz model, as reparameterised by Zwietering et al. (1990) from the modified Gompertz model.

\[Y_{(t)} = Y_0 + (Y_{max} - Y_0) \times exp \left[-exp \left( \frac{\mu_{max}\times (\lambda -t)}{Y_{max}-Y_0} + 1 \right) \right]\]

2.2 No stationary phase growth models

predmicror can adjust three nonlinear models to microbial growth curves without stationary phase: reduced Huang model, reduced Baranyi & Roberts model and two-phase linear growth model.

2.2.1 Huang model

This model is a special case of the complete Huang model, suitable for experimental growth curves that do not reach stationary phases.

\[Y_{(t)} = Y_0 + \mu_{max} \times \left[ t + 0.25 \times log \left( \frac{1 + e^{-4\times(t-\lambda)}}{1 + e^{4\times\lambda}} \right) \right]\]

2.2.2 Baranyi & Roberts model

This is a special case of the full Baranyi & Roberts model briefly presented above.

\[Y_{(t)} = Y_0 + \mu_{max} \times t + log \left[exp(-\mu_{max} \times t) + exp(-\mu_{max} \times \lambda) - exp(-\mu_{max} \times t-\mu_{max} \times \lambda) \right]\]

2.2.3 Two-phase linear model

This model is a reduced model of the three-phase linear growth model proposed by Buchanan et al. (1997).

\[ Y_{(t)} = \begin{cases} Y_0, & \text{if t $\leq$ $\lambda$} \\ Y_0 + \mu_{max} \times (t - \lambda), & \text{if t > $\lambda$} \end{cases} \]

2.3 No lag phase growth models

predmicror can adjust two nonlinear models to microbial growth curves that do not show lag phase: Richards model and Fang model.

2.3.1 Richards model

\[Y_{(t)} = Y_0 + \mu_{max} \times t - \frac{1}{m} \times log( 1 + \frac{exp(m \times \mu_{max} \times t) - 1}{exp(m \times(Y_{max}-Y_0))}\]

2.3.2 Fang model

Fang et al. (2012) and Fang et al. (2013) integrated the logistic growth model, producing a continuous model that is particularly suitable for growth curves without lag phase.

\[Y_{(t)} = Y_0 + Y_{max} - log \left[ e^{Y_0} + \left( e^{Y_{max}} - e^{Y_0} \right) \times e^{-\mu_{max} \times t} \right]\]

3 The cardinal parameter model

Predictive Microbiology deals with the development of accurate and versatile mathematical models, able to describe the evolution of microorganisms in food products as a function of environmental conditions, which are assumed to be measurable. Although there are a few classification schemes of predictive microbiology models, they have been traditionally classified into primary and secondary models.

predmicror can be used to fit cardinal parameter models, which are secondary models that describe the growth rate of microorganisms as a function of extrinsic and/or intrinsic factors. These are models that estimate the optimum growth rate, and the minimum, optimum and maximum values of extrinsic and intrinsic factors (e.g. temperature, pH, water activity) that characterise the growth of a given microbial strain.

The general cardinal parameter model used to describe and predict the effect of different environmental factors on the growth rate of a microorganism is based on a modular approach called the gamma concept (Zwietering et al., 1991), described as,

\[ \mu _{max}=\mu _{opt}\times \gamma \left(T \right)\times \gamma \left(pH\right)\times \gamma \left(aw\right)\times \gamma (Inh) \] where:

$\mu _{max}$: maximum growth rate (h^-1 or day^-1) of the studied bacterial strain
$\mu _{opt}$: optimum growth rate (h^-1 or day^-1) of the studied bacterial strain
$\gamma \left(T\right),\gamma \left(pH\right),\gamma \left(aw\right),\gamma (Inh)$: dimensionless functions describing the relative effects of temperature (T), pH, water activity (aw) and different measurable inhibitors (Inh) like undissociated organic acids or $CO_2$.

The functions $\gamma \left(T\right),\gamma \left(pH\right),\gamma \left(aw\right),\gamma (inh)$ have a range between 0 and 1, $\gamma =0$ when growth is fully inhibited and $\gamma =1$ when growth is not inhibited at all by the factor.

3.1 Cardinal Parameters

Many $\gamma$-type functions have been proposed for temperature, pH, aw and lactic acid. The predmicror adjusts the cardinal parameter model using the general equation for $\gamma$ proposed by Rosso et al. (1995).

\[ \gamma (X)_n=\left\{\begin{matrix} 0, \qquad X \leq X_{min} \\ \frac{(X-X_{max})\times (X-X_{min})^n}{(X_{opt}-X_{min})^{n-1}\times \left[\begin{matrix}\left(X_{opt}-X_{min}\right)\times \left(X-X_{opt}\right)-\left(X_{opt}-X_{max}\right)\times \\ \left(\left(n-1\right)\times X_{opt}+X_{min}-n \times X\right)\end{matrix}\right]}, \qquad X_{min}<X<X_{max} \\ 0, \qquad X{\geq}X_{max}\end{matrix}\right. \]

$X$: intrinsic or extrinsic factor under study; temperature, pH or aw
$X_{min}$: value of the factor below which no growth occurs
$X_{max}$: value of the factor above which no growth occurs
$X_{opt}$: value at which bacterial growth is optimum
$n$: shape parameter ( $n=2$ for temperature and water activity; and $n=1$ for pH).
$X_{min}$, $X_{max}$ and $X_{opt}$ are known as cardinal parameters, and are estimated by fitting the cardinal parameter model to growth data from experiments carried out in broth.

More precisely, $T_{min}$, $T_{max}$ and $T_{opt}$ are determined from
$\gamma \left(T\right)$; $pH_{min}$, $pH_{max}$ and $pH_{opt}$ are determined from $\gamma \left(pH\right)$; and $aw_{min}$, $aw_{max}$ and $aw_{opt}$ are determined from $\gamma \left(aw\right)$. Often, when adjusting the cardinal parameter model for aw, $aw_{max}$ is set to one, because it is in effect the maximum value of the water activity measurement.

4 Cardinal parameter models available in predmicror

4.1 Cardinal parameter model for temperature

The predimicror adjusts the Cardinal Temperature Model with Inflection (CTMI) (Rosso et al., 1993) to determine optimum growth rate ( $\mu_{opt}$) and the cardinal parameters $T_{min}$, $T_{max}$ and $T_{opt}$.

\[ \sqrt{\mu _{max}}=\sqrt{\mu _{opt}\times \gamma \left(T\right)} + \varepsilon \]

\[ \gamma (T)=\left\{\begin{matrix} 0, \qquad T \leq T_{min} \\ \frac{(T-T_{max}) \times (T-T_{min})^2}{\left(T_{opt}-T_{min}\right)\times\left[\begin{matrix}\left(T_{opt}-T_{min}\right)\times \left(T-T_{opt}\right)-\left(T_{opt}-T_{max}\right) \times\\ \left(T_{opt}+T_{min}-2T\right)\end{matrix}\right]}, \qquad T_{min} < T < T_{max}\\ 0, \qquad T \geq T_{max}\end{matrix}\right. \]

To fit this model to the growth data, the response variable, maximum growth rate ( $\mu _{max}$), is square-root transformed to reduce heterocedasticity. The residuals are represented by $\varepsilon$, and are assumed to follow a normal distribution with mean zero and variance $\sigma^2$.

4.2 Cardinal parameter model for pH

To determine optimum growth rate ($\mu_{opt}$) and the cardinal parameters $pH_{min}$, $pH_{max}$ and $pH_{opt}$, predmicror adjusts the cardinal model for pH proposed by Le Marc et al. (2002).

\[ \sqrt{\mu _{max}}=\sqrt{\mu _{opt}.\gamma \left(pH\right)}+\varepsilon \]

\[ \gamma (pH)=\left\{\begin{matrix}0,\qquad pH{\leq}pH_{min}\\\frac{(pH-pH_{max}){\times}\left(pH-pH_{min}\right)}{\left[\left(pH_{opt}-pH_{min}\right).\left(pH-pH_{opt}\right)-\left(pH_{opt}-pH_{max}\right).\left(pH_{min}-pH\right)\right]},\qquad pH_{min}<pH<pH_{max}\\0,\qquad pH{\geq}pH_{max}\end{matrix}\right. \]

4.2.1 Cardinal parameter model for Aw

predmicror adjusts the cardinal model for water activity from Rosso et al. (1993) to extract optimum growth rate ($\mu_{opt}$) and the cardinal parameters $aw_{min}$ and $aw_{opt}$.

\[ \sqrt{\mu _{max}}=\sqrt{\mu _{opt} \times \gamma \left(aw\right)} + \varepsilon \]

\[ \gamma (aw)=\left\{\begin{matrix} 0, \qquad aw \leq aw_{min} \\ \frac{(aw-1.0)\times(aw-aw_{min})^2}{\left(aw_{opt}-aw_{min}\right)\times\left[\begin{matrix}\left(aw_{opt}-aw_{min}\right){\times}\left(aw-aw_{opt}\right)-\left(aw_{opt}-1.0\right)\times \\ \left(aw_{opt}+aw_{min}-2aw\right)\end{matrix}\right]}, \qquad aw_{min}<aw<1.0 \\ 0, \qquad aw \geq 1.0 \end{matrix} \right. \]

To fit this model to the growth data, the response variable, maximum growth rate ( $\mu_{max}$), is square-root transformed to reduce heterocedasticity. The residuals are represented by $\varepsilon$, and are assumed to follow a normal distribution with mean zero and variance $\sigma^2$.

4.2.2 Cardinal parameter model for inhibitory substance

For the inhibitors (Inh) including undissociated organic acids, $CO$ and others, the cardinal parameter model proposed by Le Marc et al. (2002))and Coroller et al. (2005) can be fitter by the predmicror.

\[ \sqrt{\mu_{max}}=\sqrt{\mu_{opt} \times \gamma \left(inh\right)}+\varepsilon \]

\[ \gamma \left(Inh\right)=1-\left(\frac{Inh}{MIC}\right)^\alpha \]

Inh: concentration of the inhibiting substance or compound (e.g. undissociated organic acid (mM), CO (%)
MIC: Minimum Inhibitory Concentration (mM or %, accordingly)
$\alpha$: shape parameter of the curve ($\alpha=1$ the shape is linear; $\alpha>1$ the shape is downward concave; and $\alpha<1$ the shape is upward concave).

To fit this model to the growth data, the response variable, maximum growth rate ( $\mu_{max}$), is square-root transformed to reduce heterocedasticity. The residuals are represented by $\varepsilon$, and are assumed to follow a normal distribution with mean zero and variance $\sigma^2$. The model parameters are MIC and $\alpha$.

References

Baranyi, J., & Roberts, T. A. (1994). A dynamic approach to predicting bacterial growth in food. International Journal of Food Microbiology, 23, 277–294.

Buchanan, R. L., Whiting, R. C., & Damert, W. C. (1997). When is simple good enough: A comparison of the gompertz, baranyi, and three-phase linear models for fitting bacterial growth curves. Food Microbiology, 14(4), 313–326. https://doi.org/10.1006/fmic.1997.0125

Coroller, L., Guerrot, V., Huchet, V., Le Marc, Y., Mafart, P., Sohier, D., & Thuault, D. (2005). Modelling the influence of single acid and mixture on bacterial growth. International Journal of Food Microbiology, 100(1-3), 167–178. https://doi.org/10.1016/j.ijfoodmicro.2004.10.014

Fang, T., Gurtler, J. B., & Huang, L. (2012). Growth kinetics and model comparison of cronobacter sakazakii in reconstituted powdered infant formula. Journal of Food Science, 77(9), E247–E255. https://doi.org/10.1111/j.1750-3841.2012.02873.x

Fang, T., Liu, Y., & Huang, L. (2013). Growth kinetics of listeria monocytogenes and spoilage microorganisms in fresh-cut cantaloupe. Food Microbiology, 34(1), 174–181. https://doi.org/10.1016/j.fm.2012.12.005

Huang, L. (2008). Growth kinetics of listeria monocytogenes in broth and beef frankfurters-determination of lag phase duration and exponential growth rate under isothermal conditions. Journal of Food Science, 73(5), E235–E242. https://doi.org/10.1111/j.1750-3841.2008.00785.x

Huang, L. (2013). Optimization of a new mathematical model for bacterial growth. Food Control, 32(1), 283–288. https://doi.org/10.1016/j.foodcont.2012.11.019

Le Marc, Y., Huchet, V., Bourgeois, C. M., Guyonnet, J. P., Mafart, P., & Thuault, D. (2002). Modelling the growth kinetics of listeria as a function of temperature, pH and organic acid concentration. International Journal of Food Microbiology, 73(2-3), 219–237. https://doi.org/10.1016/S0168-1605(01)00640-7

Rosso, L., Bajard, S., Flandrois, J. P., Lahellec, C., Fournaud, J., & Veit, P. (1996). Differential growth of listeria monocytogenes at 4 and 8 ºC: Consequences for the shelf life of chilled products. Journal of Food Protection, 59(9), 944–949. https://doi.org/10.4315/0362-028X-59.9.944

Rosso, L., Lobry, J. R., & Flandrois, J. P. (1993). An unexpected correlation between cardinal temperatures of microbial growth highlighted by a new model. Journal of Theoretical Biology, 162(4), 447–463. https://doi.org/10.1006/jtbi.1993.1099

Rosso, L., Lobry, J., Charles (Bajard), S., & Flandrois, J.-P. (1995). Convenient model to describe the combined effects of temperature and pH on microbial growth. Applied and Environmental Microbiology, 61, 610–616. https://doi.org/10.1128/AEM.61.2.610-616.1995

Zwietering, M. H., DEKoos, J. T., Hasenack, B. E., DeWit, J. C., & K.Van’tRiet. (1991). Modeling of bacterial growth as a function of temperature. Appl. Environ. Microbiol, 57, 1094–1101.

Zwietering, M. H., Jongenburger, I., Rombouts, F. M., & van’t Riet, K. (1990). Modeling of the bacterial growth curve. Applied and Environmental Microbiology, 56(6), 1875–1881. https://doi.org/10.1128/aem.56.6.1875-1881.1990

Introduction to predmicror

Vasco Cadavez

Ursula Gonzales-Barron

2026-06-13

1 Introduction