REINTERPRETATION OF MICROBIAL SURVIVAL CURVES

The heat inactivation of microbial. spores and the mortality of vegeta tive cells exposed to heat or a hostile environment have been traditio nally assumed to be governed by first-order reaction kinetics. The con cept of thermal death time and the standard methods of calculating the safety of commercial heat preservation processes are also based on th is assumption. On closer scrutiny, however, at least some of the semil ogarithmic survival curves, which have been considered linear are in f act slightly curved. This curvature can have a significant effect on t he thermal death time, which is determined by extrapolation. The latte r can be considerably smaller or larger depending on whether the semil ogarithmic survival curve has downward or an upward concavity and how the experimenter chooses to calculate decimal reduction time. There ar e also numerous reports of organisms whose semilogarithmic survival cu rves are clearly and characteristically nonlinear, and it is unlikely that these observations are all due to a mixed population or experimen tal artifacts, as the traditional explanation implies. An alternative explanation is that the survival curve is the cumulative form of a tem poral distribution of lethal events. According to this concept each in dividual organism, or spore, dies, or is inactivated, at a specific ti me. Because there is a spectrum of heat resistances 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 p roperties. Thus, semilogarithmic survival curves whether linear or wit h an upward or a downward concavity are only reflections of heat resis tance distributions having a different, mode, variance, and skewness, and not of mortality kinetics of different orders. The concept is demo nstrated 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 distributions of resistances as a first app roximation, although alternative distribution functions can also be us ed. Changes in growing or environmental condition shift the resistance s 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 th eir manifestation in a given microbial population. However, it is cons istent with the notion that the actual destruction of a critical syste m or target is a probabilistic process that is due, at least in part, to the natural variability that exists in microbial populations.