József Baranyi

A dynamic approach to predicting bacterial growth in food.

A new member of the family of growth models described by Baranyi et al. (1993a) is introduced in which the physiological state of the cells is represented by a single variable. The duration of lag is determined by the value of that variable at inoculation and by the post-inoculation environment. When the subculturing procedure is standardized, as occurs in laboratory experiments leading to models, the physiological state of the inoculum is relatively constant and independent of subsequent growth conditions. It is shown that, with cells with the same pre-inoculation history, the product of the lag parameter and the maximum specific growth rate is a simple transformation of the initial physiological state. An important consequence is that it is sufficient to estimate this constant product and to determine how the environmental factors define the specific growth rate without modelling the environment dependence of the lag separately. Assuming that the specific growth rate follows the environmental changes instantaneously, the new model can also describe the bacterial growth in an environment where the factors, such as temperature, pH and aw, change with time.

Mathematics of predictive food microbiology

Commonly encountered problems related to modelling bacterial growth in food are analysed from a mathematical point of view. Modelling techniques and terms, some misused, are discussed and an attempt is made to clarify how, and under what conditions, they may be used. A theoretical framework is given to provide a basis in which mathematical models having been used in predictive microbiology can be embedded. By using several simplifying idealizations as a compromise between the complexity of the biological system and the available data, a practically usable model becomes available.

Predicting growth of Brochothrix thermosphacta at changing temperature

A dynamic growth model was tested using Brochothrix thermosphacta incubated in broth at changing temperatures. The model successfully predicted growth in the temperature range 5-25 degrees C when temperature increased or decreased gradually and also when temperature underwent frequent sudden changes. When the temperature profile contained step changes from 20-25 degrees C to 3 degrees C the observed growth curve deviated from that predicted by the model.

Predicting fungal growth: the effect of water activity on Aspergillus flavus and related species

Growth of four species belonging to Aspergillus Section Flavi (A. flavus, A. oryzae, A. parasiticus and A. nomius) was studied at 30 degrees C at ten water activities (aw) between 0.995 and 0.810 adjusted with equal mixtures of glucose and fructose. Colony diameters were measured at intervals and plotted against time. A flexible growth model describing the change in colony diameter (mm) with respect to time was first fitted to the measured growth data and from the fitted curves the maximum colony growth rates were calculated. These values were then fitted with respect to aw to predict colony growth rates at any aw within the range tested. The optimum aw for each species and time to reach a colony diameter of 3 mm were also calculated.

ComBase: a common database on microbial responses to food environments.

The advancement of predictive microbiology relies on available data that describe the behavior of microorganisms in different environmental matrices. For such information to be useful to the predictive microbiology research community, data must be organized in a manner that permits efficient access and data retrieval. Here, we describe a database protocol that encompasses observations of bacterial responses to food environments, resulting in a database (ComBase) for predictive microbiology purposes. The data included in ComBase were obtained from cooperating research institutes and from the literature and are publicly available via the Internet.

The effect of inoculum size on the lag phase of Listeria monocytogenes.

The effect of inoculum size on population lag times of Listeria monocytogenes was investigated using the Bioscreen automated microtitre plate incubator and reader. Under optimum conditions, lag times were little affected by inoculum size and there was little variation between replicate inocula even at very low cell numbers. However, in media containing inhibitory concentrations of NaCl, both the mean lag time and variation between replicate inocula increased as the inoculum size became smaller. The variation in lag time of cells within a population was investigated in more detail by measuring the distribution of detection times from 64 replicate inocula containing only one or two cells capable of initiating growth. The variance of the lag time distribution increased with increasing salt concentration and was greater in exponential than in stationary phase inocula. The number of cells required to initiate growth increased from one cell under optimum conditions to 10(5) cells in medium with 1.8 M NaCl. The addition of spent medium from a stationary phase culture reduced the variance and decreased lag times. The ability to initiate growth under severe salt stress appears to depend on the presence of a resistant sub-fraction of the population, although high cell densities assist adaptation of those resistant cells to the unfavourable growth conditions by some unspecified medium conditioning effect. These results are relevant to the prediction of lag times and probability of growth from low numbers of stressed cells in food.

Predictive models as means to quantify the interactions of spoilage organisms

The purpose of this paper is to quantify the interactions of some groups of spoilage organisms that can be usually found in refrigerated meat stored in air, such as: Enterobacteriaceae, Pseudomonas, Acinetobacter, Psychrobacter, Shewanella, Carnobacterium, Lactobacillus, Leuconostoc, Brochothrix and Kurthia spp. The growth of these organisms was studied in the range of temperature 2-11 degrees C and pH 5.2-6.4, which is characteristic of refrigerated meat. The main growth parameters (maximum specific growth rate and lag time) were modelled by multivariate quadratic polynomials of temperature and pH. The interactions of the organisms were analyzed by comparing their growth models obtained in isolation with those obtained in mixture. The difference between the models was quantified by statistical F-values which were used to measure how much the growth of an organism or group of organisms was affected by others and which of them dominated their joint growth.

Observing Growth and Division of Large Numbers of Individual Bacteria by Image Analysis

ABSTRACT We describe a method that enabled us to observe large numbers of individual bacterial cells during a long period of cell growth and proliferation. We designed a flow chamber in which the cells attached to a transparent solid surface. The flow chamber was mounted on a microscope equipped with a digital camera. The shear force of the flow removed the daughter cells, making it possible to monitor the consecutive divisions of a single cell. In this way, kinetic parameters and their distributions, as well as some physiological characteristics of the bacteria, could be analyzed based on more than 1,000 single-cell observations. The method which we developed enabled us to study the history effect on the distribution of the lag times of single cells.

A predictive model for the combined effect of pH, sodium chloride and storage temperature on the growth of Brochothrix thermosphacta

Growth of Brochothrix thermosphacta was observed under ranges of pH (5.6-6.8), NaCl (0.5-8.0% w/v) and incubation temperature (1-30 degrees C). In order to compare different approaches, two models were used to fit growth curves to viable count data, and to calculate parameters from those fitted curves. Growth responses as a function of pH, NaCl and temperature were described with a quadratic function which was then used to predict growth within the limits where growth was observed. The predictions of the model show good agreement with published observations from other laboratories.

Predictions of growth for Listeria monocytogenes and Salmonella during fluctuating temperature

We studied the predictive performance of a dynamic modelling approach, combined with predictions from the Food MicroModel software, applied to the growth of Listeria monocytogenes and Salmonella in pasteurised milk, chicken liver pâté and minced chicken, under constant as well as fluctuating temperatures. We found that, in general, the accuracy of a prediction under fluctuation temperature was similar to that under constant temperature. Generally, there was a good agreement between predictions and observations. However, the growth of Listeria monocytogenes in pasteurised milk was inhibited largely by the natural flora present.