survivalmodels: Bacterial survival models

In nlstools: Tools for nonlinear regression diagnostics

Description

Formulas of primary survival models commonly used in predictive microbiology

Usage

geeraerd
geeraerd_without_Nres
geeraerd_without_Sl
mafart
albert
trilinear
bilinear_without_Nres
bilinear_without_Sl

Details

These models describe the evolution of the decimal logarithm of the microbial count (LOG10N) as a function of the time (t).

geeraerd is the model of Geeraerd et al. (2005) with four parameters (LOG10N0, kmax, Sl, LOG10Nres)

geeraerd_without_Nres is the model of of Geeraerd et al. (2005) with three parameters (LOG10N0, kmax, Sl), without tail

geeraerd_without_Sl is the model of of Geeraerd et al. (2005) with three parameters (LOG10N0, kmax, Nres), without shoulder

mafart is the Weibull model as parameterized by Mafart et al. (2002) with three parameters (p, delta, LOG10N0)

albert is the modified Weibull model proposed by Albert and Mafart (2005) with four parameters (p, delta, LOG10N0, LOG10Nres)

trilinear is the three-phase linear model with four parameters (LOG10N0, kmax, Sl, LOG10Nres)

bilinear_without_Nres is the two-phase linear model with three parameters (LOG10N0, kmax, Sl), without tail

bilinear_without_Sl is the two-phase linear model with three parameters (LOG10N0, kmax, LOG10Nres), without shoulder

Value

A formula

Author(s)

Florent Baty [e-mail]
Marie-Laure Delignette-Muller [e-mail]

References

Albert I, Mafart P (2005) A modified Weibull model for bacterial inactivation. International Journal of Food Microbiology, 100, 197-211.

Geeraerd AH, Valdramidis VP, Van Impe JF (2005) GInaFiT, a freeware tool to assess non-log-linear microbial survivor curves. International Journal of Food Microbiology, 102, 95-105.

Mafart P, Couvert O, Gaillard S, Leguerinel I (2002) On calculating sterility in thermal preservation methods : application of the Weibull frequency distribution model. International Journal of Food Microbiology, 72, 107-113.

Examples

# Example 1

data(survivalcurve1)
nls1a <- nls(geeraerd, survivalcurve1,
	list(Sl = 5, kmax = 1.5, LOG10N0 = 7, LOG10Nres = 1))
nls1b <- nls(trilinear, survivalcurve1,
	list(Sl = 5, kmax = 1.5, LOG10N0 = 7, LOG10Nres = 1))
nls1c <- nls(albert,survivalcurve1,
	list(p = 1.2, delta = 4, LOG10N0 = 7, LOG10Nres = 1))
def.par <- par(no.readonly = TRUE)
par(mfrow = c(2,2))
overview(nls1a)
plotfit(nls1a, smooth = TRUE)
overview(nls1b)
plotfit(nls1b, smooth = TRUE)
overview(nls1c)
plotfit(nls1c, smooth = TRUE)
par(def.par)

# Example 2

data(survivalcurve2)
nls2a <- nls(geeraerd_without_Nres, survivalcurve2,
	list(Sl = 10, kmax = 1.5, LOG10N0 = 7.5))
nls2b <- nls(bilinear_without_Nres, survivalcurve2, 
	list(Sl = 10, kmax = 1.5, LOG10N0 = 7.5))
nls2c <- nls(mafart, survivalcurve2,
	list(p = 1.5, delta = 8, LOG10N0 = 7.5))
def.par <- par(no.readonly = TRUE)
par(mfrow = c(2,2))
overview(nls2a)
plotfit(nls2a, smooth = TRUE)
overview(nls2b)
plotfit(nls2b, smooth = TRUE)
overview(nls2c)
plotfit(nls2c, smooth = TRUE)
par(def.par)

# Example 3

data(survivalcurve3)
nls3a <- nls(geeraerd_without_Sl, survivalcurve3,
	list(kmax = 4, LOG10N0 = 7.5, LOG10Nres = 1))
nls3b <- nls(bilinear_without_Sl, survivalcurve3,
	list(kmax = 4, LOG10N0 = 7.5, LOG10Nres = 1))
nls3c <- nls(mafart, survivalcurve3,
	list(p = 0.5, delta = 0.2, LOG10N0 = 7.5))
def.par <- par(no.readonly = TRUE)
par(mfrow = c(2,2))
overview(nls3a)
plotfit(nls3a, smooth = TRUE)
overview(nls3b)
plotfit(nls3b, smooth = TRUE)
overview(nls3c)
plotfit(nls3c, smooth = TRUE)
par(def.par)

nlstools documentation built on May 2, 2019, 5:49 p.m.