onestep

disconestep

cfu_in

pstep is one row of 
psteps &lt;- data.frame(name, id, process_id, temp_min, temp_max, 
time_min, time_max, matrix_id, factor, stepnumber)


pstep


method for determining the process step duration


pdistpsteps


method for selection of series of measured values


apsomvmethod

intexmethod

calculates the bacterial count at the end of a process step,
default method onestep assumes the set of cfus to be normally distributed, 
method disconestep uses its frequency distribution


The main method npmpm calculates bacterial concentrations
during food manufacture after a contamination. Variability and
uncertainty are included by use of probability distributions
and Monte Carlo Simulation. The model aims at predicting
possible bacterial concentrations at one certain point in time
s, e.g. at the end of a process chain. The process steps of
this process chain are run through in linear order.
Experimental data that match current process step conditions
are gathered, and one deterministic primary model is fitted to
every series of measured values. From every fitted curve one
concentration of bacteria at time s is computed, yielding a set
of concentrations. This sample of possible contamination sizes
is assumed to follow a certain probability distribution. After
calculation of distribution parameters, one value is randomly
drawn from this probability distribution. This value may be
modified, and then serves as contamination for the next process
step.

onestep: calculates the bacterial count at the end of a process step

Description

Usage

Arguments

Value