predict_inactivation {bioinactivation}

Prediction of Dynamic Inactivation

Description

Predicts the inactivation of a microorganism under isothermal or non-isothermal temperature conditions. The thermal resistence of the microorganism are defined with the input arguments.

Usage

predict_inactivation(simulation_model, times, parms, temp_profile, ...,
  tol0 = 1e-05)

Arguments

simulation_model

character identifying the model to be used.

times

numeric vector of output times.

parms

list of parameters defining the parameters of the model.

temp_profile

data frame with discrete values of the temperature for each time. It must have one column named time and another named temperature providing discrete values of the temperature at time points.

...

Additional arguments passed to ode.

tol0

numeric. Observations at time 0 make Weibull-based models singular. The time for observatins taken at time 0 are changed for this value. By default ('tol0 = 1e-5')

Details

The value of the temperature is calculated at each value of time by linear interpolation of the values provided by the input argument temp_profile. The function ode of the package deSolve is used for the resolution of the differential equation.

Value

A list of class SimulInactivation with the results. It has the following entries:

• model: character defining the model use for the prediction.

• model_parameters: named numeric vector with the values of the model parameters used.

• temp_approximations: function used for the interpolation of the temperature. For a numeric value of time given, returns the value of the temperature and its first derivative.

• simulation: A data frame with the results calculated. Its first column contains the times at which the solution has been calculated. The following columns the values of the variables of the model. The three last columns provide the values of logN, S and logS.

See Also

ode, get_model_data

Examples

## EXAMPLE 1 -----------

## Retrieve the model keys available for dynamic models.
get_model_data()

## Set the input arguments
example_model <- "Geeraerd"  # Geeraerd's model will be used
times <- seq(0, 5, length=100)  # values of time for output

model_data <- get_model_data(example_model)  # Retrive the data of the model used
print(model_data$parameters)
print(model_data$variables)
model_parms <- c(D_R = 1,
                 z = 10,
                 N_min = 100,
                 temp_ref = 100,
                 N0 = 100000,
                 C_c0 = 1000
                 )

## Define the temperature profile for the prediction
temperature_profile <- data.frame(time = c(0, 1.25, 2.25, 4.6),
                                  temperature = c(70, 105, 105, 70))

## Call the prediction function
prediction_results <- predict_inactivation(example_model, times,
                                           model_parms, temperature_profile)

## Show the results
head(prediction_results$simulation)
plot(prediction_results)
time_to_logreduction(1.5, prediction_results)

## END EXAMPLE 1 -----------