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Cantaloupe washing in flume tank

Source: R/caFlumeTank.R
caFlumeTank.Rd

The function caFlumeTank() simulates three events taking place during flume tank washing:

  1. the washing of bacteria off the cantaloupe rind at a washing efficiency of logDecWash;

  2. the elimination of bacteria in the sanitising water at a sanitising efficiency of log10SaniWash; and

  3. the redistribution of survivors on the cantaloupe rind.

Usage

caFlumeTank(
  data = list(),
  logDecWash,
  logDecSani,
  b,
  nLots = NULL,
  sizeLot = NULL
)

Arguments

data

a list of:

N

(CFU) A matrix of size nLots lots by sizeLot units representing the numbers of L. monocytogenes on the rind, from contaminated cultivation lots;

P

Prevalence of contaminated harvested lots pre-washing (scalar).

logDecWash

(log10) Reduction attained by washing (scalar or vector).

logDecSani

(log10) Reduction attained by sanitising water (scalar or vector).

b

Dispersion factor representing the clustering of surviving cells during redistribution (scalar, vector or matrix).

nLots

Number of harvested lots of cantaloupe.

sizeLot

Number of cantaloupes in a harvested lot.

Value

A list of two elements of the data objects:

N

(CFU) A matrix of size nLots lots by sizeLot units representing the numbers of L. monocytogenes on washed cantaloupes.

P

Prevalence of contaminated harvested lots post-washing (scalar).

Note

Since surviving cells are distributed in water, a value of b higher than 1 should be used to assume random homogeneous distribution. This dispersion factor is a parameter of the beta-binomial distribution (Nauta (2005) ).

References

Pouillot R, Delignette-Muller M (2010). “Evaluating variability and uncertainty in microbial quantitative risk assessment using two R packages.” International Journal of Food Microbiology, 142(3), 330-40.

Team RC (2022). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.

Nauta MJ (2005). “Microbiological risk assessment models for partitioning and mixing during food handling.” International Journal of Food Microbiology, 100(1), 311--322. doi:10.1016/j.ijfoodmicro.2004.10.027 .

Author

Regis Pouillot rpouillot.work@gmail.com

Examples

library(extraDistr)
library(mc2d)
#> Loading required package: mvtnorm
#> 
#> Attaching package: ‘mc2d’
#> The following objects are masked from ‘package:extraDistr’:
#> 
#>     dbern, ddirichlet, dtriang, pbern, ptriang, qbern, qtriang, rbern,
#>     rdirichlet, rtriang
#> The following objects are masked from ‘package:base’:
#> 
#>     pmax, pmin
dat <- caPrimaryProduction(
  nLots = 100,
  sizeLot = 100,
  cantaWeight = 800,
  pSoil = 0.089,
  pManure = 0.1,
  pIrrigRaining = 0.05,
  pFoil = 0.5,
  rFoil = 0.75,
  pIrrig = 0.131
)

WashedMelons <- caFlumeTank(dat,
  logDecWash = 1.5,
  logDecSani = 2,
  b = 2.5,
  nLots = nLots,
  sizeLot = sizeLot
)
hist(WashedMelons$N)