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Cantaloupe cross-contamination during washing in flume tank

Source: R/caFlumeTankCC.R
caFlumeTankCC.Rd

The function caFlumeTankCC() function simulates the potential contamination of cantaloupe, when in direct contact with contaminated water in flume tank. The cross-contamination algorithm accounts for four possible scenarios:

  1. cross-contamination occurring in lots already contaminated;

  2. re-contamination occurring in lots that were not contaminated;

  3. no cross-contamination occurring in lots already contaminated; and

  4. no cross-contamination occurring in lots that were not contaminated.

Usage

caFlumeTankCC(
  data = list(),
  nLots = NULL,
  sizeLot = NULL,
  cantaWeight = NULL,
  probCCW = 0.5,
  logWaterMin = 1,
  logWaterMode = 1,
  logWaterMax = 5,
  pWaterGain = 0.004,
  bWater = 1
)

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).

nLots

see Lot2LotGen() function.

sizeLot

see Lot2LotGen() function.

cantaWeight

(g) weight of a cantaloupe.

probCCW

Probability that water of flume tank is contaminated (scalar).

logWaterMin

(log10(cfu/l)) Minimal concentration of L. monocytogenes in water of flume tank (scalar or vector). The value was derived during JEMRA meeting related to unpublished data transmitted by experts

logWaterMode

(log10(cfu/l)) Mode concentration of L. monocytogenes in water of flume tank (scalar or vector). The value was derived during JEMRA meeting related to unpublished data transmitted by experts

logWaterMax

(log10(cfu/l)) Maximal concentration of L. monocytogenes in water of flume tank (scalar or vector). The value was derived during JEMRA meeting related to unpublished data transmitted by experts

pWaterGain

value of the fraction of water gain (ml) relative to the cantaloupe weight in g (\(default=0.004\) according to Richards and Beuchat (2004) ).

bWater

Dispersion factor representing the clustering of cells during flume_tank washing (scalar).

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

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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/.

Author

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Examples

dat <- caPrimaryProduction(
  nLots = 100,
    sizeLot = 100)
    Nf <- caFlumeTankCC(dat,
                   probCCW = 0.125,
                   logWaterMin = 1,
                   logWaterMode = 1,
                   logWaterMax = 5,
                   bWater = 1)
hist(Nf$N)