Several sigmoidal functions (logistic, Gompertz, Richards, Schnute, and Stannard) were compared to describe a bacterial growth curve. They were compared statistically by using the model of Schnute, which is a comprehensive model, encompassing all other models. The t test and the F test were used. With the t test, confidence intervals for parameters can be calculated and can be used to distinguish between models. In the F test, the lack of fit of the models is compared with the measuring error. Moreover, the models were compared with respect to their ease of use. All sigmoidal functions were modified so that they contained biologically relevant parameters. The models of Richards, Schnute, and Stannard appeared to be basically the same equation. In the cases tested, the modified Gompertz equation was statistically sufficient to describe the growth data of Lactobacillus plantarum and was easy to use.

The temperature of chilled foods is a very important variable for microbial safety in a production and distribution chain. To predict the number of organisms as a function of temperature and time, it is essential to model the lag time, specific growth rate, and asymptote (growth yield) as a function of temperature. The objective of this research was to determine the suitability and usefulness of different models, either available from the literature or newly developed. The models were compared by using an F test, by which the lack of fit of the models was compared with the measuring error. From the results, a hyperbolic model was selected for the description of the lag time as a function of temperature. Modified forms of the Ratkowsky model were selected as the most suitable model for both the growth rate and the asymptote as a function of temperature. The selected models could be used to predict experimentally determined numbers of organisms as a function of temperature and time.

Flow cytometry is a rapid and sensitive method which may be used for the detection of microorganisms in foods and drinks. A key requirement for this method is a sufficient fluorescence staining of the target cells. The mechanism of staining of the yeast Saccharomyces cerevisiae by fluorescein diacetate (FDA) and 5-(and 6-)carboxyfluorescein diacetate (cFDA) was studied in detail. The uptake rate of the prefluorochromes increased in direct proportion to the concentration and was not saturable, which suggests that transport occurs via a passive diffusion process. The permeability coefficient for cFDA was 1.3 ؋ 10 ؊8 m s ؊1 . Once inside the cell, the esters were hydrolyzed by intracellular esterases and their fluorescent products accumulated. FDA hydrolysis (at 40؇C) in cell extracts could be described by first-order reaction kinetics, and a rate constant (K) of 0.33 s ؊1 was calculated. Hydrolysis of cFDA (at 40؇C) in cell extracts was described by Michaelis-Menten kinetics with an apparent V max and K m of 12.3 nmol ⅐ min ؊1 ⅐ mg of protein ؊1 and 0.29 mM, respectively. Accumulation of fluorescein was most likely limited by the esterase activity, since transport of FDA was faster than the hydrolysis rate. In contrast, accumulation of carboxyfluorescein was limited by the much slower transport of cFDA through the cell envelope. A simple mathematical model was developed to describe the fluorescence staining. The implications for optimal staining of yeast cells with FDA and cFDA are discussed.

High hydrostatic pressure (HHP) inactivation of three Listeria monocytogenes strains (EGDe, LO28, and Scott A) subjected to 350 MPa at 20 degrees C in ACES buffer resulted in survival curves with significant tailing for all three strains. A biphasic linear model could be fitted to the inactivation data, indicating the presence of an HHP-sensitive and an HHP-resistant fraction, which both showed inactivation according to first-order kinetics. Inactivation parameters of these subpopulations of the three strains were quantified in detail. EGDe showed the highest D-values for the sensitive and resistant fraction, whereas LO28 and Scott A showed lower HHP resistance for both fractions. Survivors isolated from the tail of LO28 and EGDe were analyzed, and it was revealed that the higher resistance of LO28 was a stable feature for 24% (24 of 102) of the resistant fraction. These HHP-resistant variants were 10 to 600,000 times more resistant than wild type when exposed to 350 MPa at 20 degrees C for 20 min. Contrary to these results, no stable HHP-resistant isolates were found for EGDe (0 of 102). The possible effect of HHP survival capacity of stress-resistant genotypic and phenotypic variants of L. monocytogenes on the safety of HHP-processed foods is discussed.

The temperature of chilled foods is an important variable for the shelf life of a product in a production and distribution chain. To predict the number of organisms as a function of temperature and time, it is essential to model the growth as a function of temperature. The temperature is often not constant in various stages of distribution. The objective of this research was to determine the effect of shifts in temperature. The suitability and usefulness of several models to describe the growth of LactobaciUlus plantarum with fluctuating temperatures was evaluated. It can be assumed that temperature shifts within the lag phase can be handled by adding relative parts of the lag time to be completed and that temperature shifts within the exponential phase result in no lag phase. With these assumptions, the kinetic behavior of temperature shift experiments was reasonably well predicted, and this hypothesis was accepted statistically in 73% of the cases. Only shifts of temperature around the minimum temperature for growth showed very large deviations from the model prediction. The best results were obtained with the assumption that a temperature shift (within the lag phase as well as within the exponential phase) results in an additional lag phase. This hypothesis was accepted statistically in 93% of the cases. The length of the additional lag phase is one-fourth of the lag time normally found at the temperature after the shift.