Transport of RTE diced cantaloupe packs from retail to home

The function caRet2HomeRTE() simulates the growth of L. monocytogenes in RTE diced cantaloupe during transport from retail to home, and is based on the function caGrowthBaranyi(). The algorithm samples transportation time and temperature at the unit level since they depend on the consumer. The input data provides the algorithm with the lot-specific values of EGR5 and the unit-specific values of lnQt obtained from the previous logistics stage. A Gamma distribution represent the variability in transport time, whereas a Pert distribution the variability in transport temperature.

Usage

caRet2HomeRTE(
  data = list(),
  MPD = NULL,
  Tmin = -2.0196,
  tempMin,
  tempMode,
  tempMax,
  timeShape,
  timeScale
)

Arguments

data

a list of:

N

(CFU) A matrix containing the numbers of L. monocytogenes in packs of RTE diced cantaloupe at the end of retail, from contaminated lots;

P

Prevalence of contaminated lots (scalar);

lnQt

Natural log of the Q parameter at the end of retail (matrix);

lotEGR5

(\(h^{-1}\)) Growth rate of L. monocytogenes in cantaloupe flesh specific to every lot (vector);

unitSize

(g) Weight of a pack of cantaloupe dices.

MPD

(log10 CFU/g) Maximum population density of L. monocytogenes in cantaloupe flesh (scalar).

Tmin

(\(^\circ C\)) Nominal minimum temperature for growth of L. monocytogenes in cantaloupe flesh (suggested \(default=-2.0196\ ^\circ C\)) (scalar).

tempMin

(\(^\circ C\)) Minimum transportation temperature (scalar).

tempMode

(\(^\circ C\)) Mode of the transportation temperature (scalar).

tempMax

(\(^\circ C\)) Maximum transportation temperature (scalar).

timeShape

Shape parameter of the gamma distribution representing transportation time in hours (scalar) (suggested \(default=6.2\)).

timeScale

Scale parameter of the gamma distribution representing transportation time in hours (scalar) (suggested \(default=8.2\)).

Value

A list of five elements:

N

(CFU) A matrix containing the numbers of L. monocytogenes in packs of RTE diced cantaloupe at the end of transportation, from contaminated lots;

P

Prevalence of RTE diced cantaloupe lots contaminated with L. monocytogenes (scalar);

lnQt

Natural log of the Q parameter at the end of transportation (matrix);

lotEGR5

(\(h^{-1}\)) Growth rate of L. monocytogenes in cantaloupe flesh specific to every lot (vector);

unitSize

(g) Weight of a pack of cantaloupe dices.

Note

The parameter \(Tmin=-2.0196\ ^\circ C\) was determined from fitting a square-root model to data extracted from multiple sources (refer to the function caGrowthBaranyi()). Parameters for the distributions of the transportation conditions should be defined by the user and/or tested in scenarios. In addition to the final L. monocytogenes numbers, the function also returns the values of lotEGR5 and lnQt so that the L. monocytogenes growth could be followed up in subsequent logistics stages.

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

Ursula Gonzales-Barron ubarron@ipb.pt

Examples

tempMin <- 3
tempMode <- 5
tempMax <- 12
Tmin <- -2.0196
nLots <- 1000
sizeLot <- 250
N <- matrix(230, nLots, sizeLot)
N[5, ] <- 0
lnQt <- matrix(2.3, nLots, sizeLot)
lnQt[5, ] <- 0
dat <- list(
  N = N, lnQt = lnQt,
  lotEGR5 = extraDistr::rtnorm(nLots, 0.03557288, 0.004, a = 0),
  P = 0.4, unitSize = 200
)

ArriveHome <- caRet2HomeRTE(dat,
  MPD = 8.5,
  Tmin - 2.0196,
  tempMin = tempMin,
  tempMode = tempMode,
  tempMax = tempMax,
  timeShape = 6.2,
  timeScale = 8.2
)
hist(ArriveHome$N)