Reinterpretation of Microbial Survival Curves

Abstract

The heat inactivation of microbial spores and the mortality of vegetative cells exposed to heat or a hostile environment have been traditionally assumed to be governed by first-order reaction kinetics. The concept of thermal death time and the standard methods of calculating the safety of commercial heat preservation processes are also based on this assumption. On closer scrutiny, however, at least some of the semilogarithmic survival curves, which have been considered linear are in fact slightly curved. This curvature can have a significant effect on the thermal death time, which is determined by extrapolation. The latter can be considerably smaller or larger depending on whether the semilogarithmic survival curve has downward or an upward concavity and how the experimenter chooses to calculate decimal reduction time. There are also numerous reports of organisms whose semilogarithmic survival curves are clearly and characteristically nonlinear, and it is unlikely that these observations are all due to a mixed population or experimental artifacts, as the traditional explanation implies. An alternative explanation is that the survival curve is the cumulative form of a temporal distribution of lethal events. According to this concept each individual organism, or spore, dies, or is inactivated, at a specific time. Because there is a spectrum of heat resistance in the population--some organism or spores are destroyed sooner, or later, than others--the shape of the survival curve is determined by its distributions properties. Thus, semilogarithmic survival curves whether linear or with an upward or a downward concavity are only reflections of heat resistance distributions having a different, mode variance, and skewness, and not of mortality kinetics of different orders. The concept is demonstrated with published data on the lethal effect of heat on pathogens and spores alone and in combination with other factors such as pH or high pressure. Their different survival patterns are all described in terms of different Weibull distribution of resistances as a first approximation, although alternative distribution functions can also be used. Changes in growing or environmental condition shift the resistances distribution's mode and can also affect its spread and skewness. The presented concept does not take into account the specific mechanisms that are the cause of mortality or inactivation--it only describes their manifestation in a given microbial population. However, it is consistent with the notion that the actual destruction of a critical system or target is a probabilistic process that is due, at least in part, to the natural variability that exists in microbial populations.

Behavioural pattern of vegetative cells and spores of Bacillus cereus as affected by time-temperature combinations used in processing of Indian traditional foods

“…Hence, all spores in the clumps need to be inactivated prior to the destruction of colony forming ability of the clump (Adams and Moss 1997;Furukawa et al 2005). The effect could also be due to the heterogeneous sub-populations of spores (dormant, germinated and inactivated) differing in their physiological state during heat treatment and become resistant within a highly extreme resistant sub-population (Peleg and Cole 1998). The lag phase in spore inactivation pattern revealed that there was a small decrease in the concentration of spores and the linear decline phase show that specific death rate was constant.…”

Survival of Campylobacter jejuni in mineral bottled water according to difference in mineral content: Application of the Weibull model

“…The cumulative form of Weibull frequency distribution model was used to describe Campylobacter survival kinetics in water as it is suitable for quantifying Page 5 / 19 bacterial survival for both log and non-log linear survival curves. The Weibull model has largely been used to describe bacterial inactivation by thermal and nonthermal processes as stress resistance of a microbial population often follows a Weibull distribution (Cunhan et al, 1998;Peleg and Cole, 1998;Fernandez et al, 1999;van Boekel, 2002;Corradini and Peleg, 2003;Virto et al, 2005;Hajmeer et al, 2006). We used a logarithm decimal form of Weibull defined as follows (Mafart et al, 2002;van Boekel, 2002):…”

Comparative analyses of prediction models for inactivation of Escherichia coli in carrot juice by means of pulsed electric fields

“…Based on these types of variations, Peleg proposed a model for microbial inactivation, known as Peleg Model (Peleg 1995;Peleg and Cole 1998) which represents the field strength and survival fraction as…”

“…This model is a mathematical equation based on Weibull distribution function (Peleg and Cole 1998;Van Boekel 2002) which describes the survival curves non-logarithmically as…”

“…These models colligate the various PEF treatment parameters to survival fractions in order to estimate the inactivation level (Hülsheger et al 1981;Peleg 1995;Peleg and Cole 1998;Van Boekel 2002). Usually for the reduction of few log 10 cycles, the survival curves follow the first order log-linear relationship.…”