Effect of blanching on L. monocytogenes in processed vegetables

The function fvBlanching() describes the effect of blanching on the numbers of L. monocytogenes present in vegetables such as broccoli, mushroom, onions, peas and pepper. The function is based on the Bigelow model, which describes the decimal reduction time (D) as a function of temperature, with parameters z and reference D (Dref) at 70 \(^\circ C\). This function assumes that the extent of reduction is global for the aforementioned vegetables, and depends on the duration and temperature of blanching. Whereas the algorithm considers the z value as fixed, Dref is assumed to be strain-specific, and thus different between lots.

Usage

fvBlanching(
  data = list(),
  nLots = NULL,
  tempBlanch = 83,
  timeBlanch = 0.75,
  logDrefMean = -1.78,
  logDrefSd = 0.251,
  zT = 6.06
)

Arguments

data

a list of:

N: (CFU) A matrix of size nLots lots by sizeLot units containing the numbers of L. monocytogenes in pre-blanching units or portions from contaminated lots;
ProbUnitPos: Probability that the lot is contaminated (a lot is considered contaminated if at least one unit or portion is contaminated) (vector);
P: Mean prevalence of contaminated lots (scalar).

nLots

Number of lots sampled or size of the Monte Carlo simulation (scalar).

tempBlanch

(\(^\circ C\)) Temperature of blanching (scalar or vector).

timeBlanch

(min) Duration of blanching (scalar or vector).

logDrefMean

Mean parameter of the normal distribution representing the variability in the log 10 of the reference D (min) at 70 \(^\circ C\) (scalar or vector).

logDrefSd

Standard deviation of the normal distribution representing the variability in the log 10 of the reference D (min) at 70 \(^\circ C\) (scalar or vector).

zT

fixed z value characterising the effect of temperature on D for L. monocytogenes in vegetables (scalar).

Value

A list of four elements:

N: (CFU) A matrix of size nLots lots by sizeLot units representing the numbers of L. monocytogenes per unit or portion after blanching;
ProbUnitPos: Probability that the lot is contaminated after blanching (a lot is considered contaminated if at least one unit or portion remained contaminated after blanching) (vector);
P: Mean prevalence of contaminated lots after blanching (scalar);
pSurviveBlanching: Probability of a microbial cell to survive the blanching process (vector).

Note

If no more bacteria are in a lot, one cell will be set in one unit of that lot. The heat resistance parameters suggested for use (\(zT=6.06\ ^{\circ} C\), and \(log10\ Dref\_mean=-1.78\) and \(log10Dref\_sd=0.251\) at the reference temperature of 70 \(^{\circ} C\)) were obtained from fitting a Bigelow model to survival data of L. monocytogenes in broccoli, mushrooms, onions, peas and pepper extracted from Mazzotta (2001)

References

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

Mazzotta AS (2001). “Heat resistance of Listeria monocytogenes in vegetables: evaluation of blanching processes.” Journal of Food Protection, 64, 385-7. doi:10.4315/0362-028x-64.3.385 .

Author

Laurent Guillier

Examples

dat <- Lot2LotGen(
  nLots = 1000, sizeLot = 1000, unitSize = 500,
  betaAlpha = 0.5112, betaBeta = 9.959, C0MeanLog = 1.023, C0SdLog = 0.3267,
  propVarInter = .7
)
res <- fvBlanching(dat,
  tempBlanch = 85,
  timeBlanch = 1,
  logDrefMean = -1.78,
  logDrefSd = 0.251,
  zT = 6.06
)
res$P
#> [1] 0.7569237
hist(c(res$N))