Mathematical Modelling of (Dynamic) Microbial Inactivation
Isothermal Arrhenius model
Isothermal Bigelow's Model
Continuum Interpolation of Discrete Temperatures Values
Correctness Check of Model Parameters
First derivative of the Arrhenius model
First Derivate of the Linear Bigelow Model
First Derivate of Geeraerd's Model
First Derivate of the Weibull-Mafart Model
First Derivate of the Metselaar Model
First Derivate of the Weibull-Peleg Model
Fitting of Dynamic Inactivation Models
Fitting of dynamic inactivation with MCMC
Fit of Isothermal Experiments
Isothermal Geeraerd Model
Isothermal Model Data
Mapping of Simulation Model Functions
Error of the Prediction of Microbial Inactivation
Goodness of fit for Dynamic fits
Goodness of fit for Isothermal fits
Goodness of fit for MCMC fits
Goodness of fit for microbial inactivation models
Test of FitInactivation object
Test of FitInactivationMCMC object
Test of IsoFitInactivation object
Test of PredInactivationMCMC object
Test of SimulInactivation object
Isothermal Metselaar model
Plot of FitInactivation Object
Plot of FitInactivationMCMC Object
Plot of IsoFitInactivation Object
Plot of PredInactivationMCMC Object
Plot of SimulInactivation Object
Dynamic Prediction Intervals from a Monte Carlo Adjustment
Prediction of Dynamic Inactivation
Random sample of the parameters of a FitInactivation object
Random sample of the parameters of a IsoFitInactivation object
Random sample of the parameters of a FitInactivationMCMC object
Summary of a FitInactivation object
Summary of a FitInactivationMCMC object
Summary of a IsoFitInactivation object
Time to reach X log reductions
Isothermal Weibull-Mafart Model
Isothermal Weibull-Peleg Model
Functions for modelling microbial inactivation under isothermal or dynamic conditions. The calculations are based on several mathematical models broadly used by the scientific community and industry. Functions enable to make predictions for cases where the kinetic parameters are known. It also implements functions for parameter estimation for isothermal and dynamic conditions. The model fitting capabilities include an Adaptive Monte Carlo method for a Bayesian approach to parameter estimation.