Using the data set “gomp1.csv”, find the parameters of the reparameterised Gompertz model.
\[\begin{equation} y= y_0 + (y_{max} -y_0)*exp(-exp(k*(lag-x)/(y_{max}-y_0) + 1) ) \end{equation}\]
Import the data set.
dat <- read.csv("gomp1.csv", sep=";", header=T) plot(dat$Time, dat$logN) str(dat) ## 'data.frame': 13 obs. of 2 variables: ## $ Time: int 0 2 4 6 8 10 12 14 16 18 ... ## $ logN: num -0.105 0.108 -0.111 0.734 2.453 ... The next step is define the Gompertz function.
The data set “FirstOrder.csv” contains observations of microbial concentrations (log N) measured at different times (t) at a given environmental condition. Lets fit a first-order growth kinetics model \(log N = log N_0 + k \times t\) to the experimental data.
Let’s import the “FirstOrder.csv” dataset, and observe the first five lines.
dat <- read.csv("FirstOrder.csv", sep=";", header=TRUE) dat ## Time N ## 1 0 37.298 ## 2 1 56.149 ## 3 2 81.
This study compares dynamic tertiary and competition models for L. monocytogenes growth as a function of intrinsic properties of a traditional Brazilian soft cheese and the inhibitory effect of lactic acid bacteria (LAB) during refrigerated storage. …