External cross-contamination of fish fillets during smearing with salt, sugar or spices

The function sfSmearingCC() simulates the potential external contamination of fish fillets during the process of dry-salting when producing smoked salmon, or during smearing the fish fillets with salt, sugar and spices when producing gravad or any macerated fish. The algorithm evaluates the cross-contamination event at fish fillet level (and not at lot level), assuming that every fish fillet has the same probability pccSmearing of being contaminated when in contact with contaminated environmental elements during the process of smearing with salt, sugar and/or spices.

Usage

sfSmearingCC(
  data = list(),
  pccSmearing,
  trSmearingMean,
  trSmearingSd,
  nSurface
)

Arguments

data

a list of

N: (CFU) A matrix of size nLots lots by sizeLot units containing the numbers of L. monocytogenes on fish fillets;
P: Mean prevalence of contaminated lots (scalar);
ProbUnitPos: Probability of individual lots being contaminated (vector).

pccSmearing

Probability that cross-contamination with L. monocytogenes occurs during dry salting, or smearing the fillets with sugar/spices, through tables or other surfaces (scalar).

trSmearingMean

Mean parameter of the normal distribution representing the variability in the log 10 of the transfer coefficient of L. monocytogenes cells from surfaces to fish fillets (scalar or vector).

trSmearingSd

Standard deviation parameter of the normal distribution representing the variability in the log 10 of the transfer coefficient of L. monocytogenes from surfaces to fish fillets (scalar or vector).

nSurface

(CFU) Numbers of L. monocytogenes cells on environmental elements in contact with fish fillets while dry salting (or while smearing the fillets with sugar/spices) (scalar or vector).

Value

A list of three elements:

N: (CFU) A matrix of size nLots lots by sizeLot units containing the numbers of L. monocytogenes in (dry-salted) fish;
ProbUnitPos: Probability of individual lots being contaminated after smearing with ingredients (vector);
P: Mean prevalence of contaminated lots after smearing with ingredients (scalar).

Note

The suggested parameters trSmearingMean=-0.29 and trSmearingSd=0.31 for the normal distribution about the variability in the log 10 of the transfer coefficient of L. monocytogenes were taken from Hoelzer et al. (2012) , to represent cross-contamination from board to meat. The values of pccSmearing and nSurface must be defined by the user and/or assessed in scenarios.

References

Hoelzer K, Pouillot R, Gallagher D, Silverman MB, Kause J, Dennis SB (2012). “Estimation of Listeria monocytogenes transfer coefficients and efficacy of bacterial removal through cleaning and sanitation.” International Journal of Food Microbiology, 157, 267-77. doi:10.1016/j.ijfoodmicro.2012.05.019 . Wolodzko T (2020). extraDistr: Additional Univariate and Multivariate Distributions. R package version 1.9.1, https://CRAN.R-project.org/package=extraDistr. Team RC (2022). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/. FDA (2021). “FDA-iRISK 4.2 Food Safety Modeling Tool: Technical Document.” U.S. Food and Drug Administration.

Author

Ursula Gonzales-Barron ubarron@ipb.pt and Regis Pouillot rpouillot.work@gmail.com

Examples


dat <- Lot2LotGen(
                  nLots = 50,
                  sizeLot = 100,
                  unitSize = 500,
                  betaAlpha = 0.5112,
                  betaBeta = 9.959,
                  C0MeanLog = 1.023,
                  C0SdLog = 0.3267,
                  propVarInter = 0.7
                  )
Nf <- sfSmearingCC(
                   dat,
                   pccSmearing = 0.05,
                   trSmearingMean = -0.29,
                   trSmearingSd = 0.31,
                   nSurface = 200
                   )
str(Nf)                    
#> List of 10
#>  $ Lot2LotGenParameters:List of 9
#>   ..$ nLots       : num 50
#>   ..$ sizeLot     : num 100
#>   ..$ unitSize    : num 500
#>   ..$ betaAlpha   : num 0.511
#>   ..$ betaBeta    : num 9.96
#>   ..$ C0MeanLog   : num 1.02
#>   ..$ C0SdLog     : num 0.327
#>   ..$ propVarInter: num 0.7
#>   ..$ Poisson     : logi FALSE
#>  $ lotMeans            : num [1:50] 0.3625 0.1263 0.0732 0.8527 0.4082 ...
#>  $ unitsCounts         : num [1:5000] 0 0 0 20 0 ...
#>  $ N                   : num [1:50, 1:100] 0 0 0 10003 0 ...
#>  $ ProbUnitPos         : num [1:50] 1 1 1 1 1 1 1 1 1 1 ...
#>  $ P                   : num 1
#>  $ betaGen             : num [1:50] 0.03298 0.00493 0.00688 0.08726 0.00792 ...
#>  $ nLots               : num 50
#>  $ sizeLot             : num 100
#>  $ unitSize            : num 500
#>  - attr(*, "class")= chr "qraLm"
Nf$P
#> [1] 1
mean(Nf$ProbUnitPos)
#> [1] 1
hist(Nf$N)