Cleaning cantaloupes by brushing

The function caBrush() models the removal of bacteria during the brushing or scrubbing step. The mean log10 reduction due to brushing must be provided.

Usage

caBrush(data = list(), nLots = NULL, sizeLot = NULL, logDecBrush)

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 (scalar).

nLots

see Lot2LotGen() function.

sizeLot

see Lot2LotGen() function.

logDecBrush

Mean log10 reduction attained by brushing or scrubbing (scalar, vector of size the number of N rows (representing lot-to-lot variability), or vector of size the number of N columns (representing element-to-element variability)).

Value

A list of two elements of the data objects:

N: (CFU) A matrix of size nLots lots by sizeLot units containing the numbers of L. monocytogenes on brushed cantaloupes
P: Prevalence of contaminated harvested lots post-brushing (scalar).

Note

Abrasive brushing for cantaloupe cleaning has been shown to be efficient in removing biofilms of L. monocytogenes attached to the rind in Fu et al. (2020) .

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/.

Fu Y, Bhunia AK, Yao Y (2020). “Abrasive brushing reduces pathogen biofilms at cantaloupe rind surface.” International Journal of Food Microbiology, 329, 108685. doi:10.1016/j.ijfoodmicro.2020.108685 .

Author

Regis Pouillot rpouillot.work@gmail.com

Examples

sizeLot <- 100
nLots <- 500
df <- list(N=matrix(rpois(sizeLot * nLots, 10),
             nrow = nLots, ncol = sizeLot),
              P=0.7, cantaWeight=1000, sizeLot=100,
              nLots=500)
brushed <- caBrush(df,
                   logDecBrush = runif(nLots, min = 0, max = 10)
                )
hist(brushed$N)