Predict microbial inactivation under dynamic environmental conditions — predict_dynamic

predict_dynamic_inactivation() solves a dynamic Weibull-Peleg type inactivation model over a time-varying environmental profile. The output is returned on the base-10 logarithmic scale.

Usage

predict_dynamic_inactivation(
  profile,
  model = "weibull_peleg",
  secondary = "constant",
  start,
  times = NULL,
  dt = 0.01,
  method = "rk4",
  temperature = "temperature"
)

Arguments

profile: A dynamic_profile() object or data frame with a time column.
model: Character string. Currently only "weibull_peleg" is supported.
secondary: Character string defining the secondary model for the Peleg rate parameter b. Use "constant" for constant conditions or "z_value" for a log-linear temperature effect.
start: Named list. Supply logN0, shape parameter n, and either b for secondary = "constant" or b_ref, T_ref, and z for secondary = "z_value".
times: Optional numeric vector of output times.
dt: Maximum integration step used by the internal RK4 solver.
method: Solver method. Currently only "rk4" is implemented.
temperature: Name of the temperature column in profile.

Value

A predmicror_dynamic_prediction data frame with time, logN, log_survival, temperature, and metadata attributes.

Examples

profile <- dynamic_profile(time = c(0, 10, 20), temperature = c(55, 58, 60))
pred <- predict_dynamic_inactivation(
  profile = profile,
  start = list(logN0 = 7, b_ref = 0.15, T_ref = 55, z = 8, n = 1.2),
  secondary = "z_value",
  dt = 0.25
)
head(pred)
#> dynamic_inactivation prediction
#>   model: weibull_peleg
#>   secondary: z_value
#>   rows: 6
#>   time response     logN log_survival temperature
#> 1 0.00 7.000000 7.000000   0.00000000      55.000
#> 2 0.25 6.974179 6.974179  -0.02582092      55.075
#> 3 0.50 6.936086 6.936086  -0.06391380      55.150
#> 4 0.75 6.892892 6.892892  -0.10710807      55.225
#> 5 1.00 6.845659 6.845659  -0.15434062      55.300
#> 6 1.25 6.794898 6.794898  -0.20510161      55.375