Growth under dynamic conditions
Predicts population growth under dynamic conditions based on the Baranyi model (Baranyi and Roberts, 1994) and secondary models based on the gamma concept (Zwietering et al. 1992).
predict_dynamic_growth( times, env_conditions, primary_pars, secondary_models, ..., check = TRUE, formula = . ~ time )
times |
Numeric vector of storage times to make the predictions |
env_conditions |
Tibble describing the variation of the environmental
conditions during storage. It must have with the elapsed time (named |
primary_pars |
A named list defining the parameters of the primary model
and the initial values of the model variables. That is, with names |
secondary_models |
A nested list describing the secondary models. |
... |
Additional arguments for |
check |
Whether to check the validity of the models. |
formula |
An object of class "formula" describing the x variable.
|
Model predictions are done by linear interpolation of the environmental
conditions defined in env_conditions.
An instance of DynamicGrowth.
## Definition of the environmental conditions
library(tibble)
my_conditions <- tibble(time = c(0, 5, 40),
temperature = c(20, 30, 35),
pH = c(7, 6.5, 5)
)
## Definition of the model parameters
my_primary <- list(mu_opt = 2,
Nmax = 1e8,N0 = 1e0,
Q0 = 1e-3)
sec_temperature <- list(model = "Zwietering",
xmin = 25, xopt = 35, n = 1)
sec_pH = list(model = "CPM",
xmin = 5.5, xopt = 6.5,
xmax = 7.5, n = 2)
my_secondary <- list(
temperature = sec_temperature,
pH = sec_pH
)
my_times <- seq(0, 50, length = 1000)
## Do the simulation
dynamic_prediction <- predict_dynamic_growth(my_times,
my_conditions, my_primary,
my_secondary)
## Plot the results
plot(dynamic_prediction)
## We can plot some environmental factor with add_factor
plot(dynamic_prediction, add_factor = "temperature", ylims= c(0, 8),
label_y1 = "Microbial count (log CFU/ml)",
label_y2 = "Storage temperature (C)")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.