Search PubMed⌕ Search

Biomedical subjects

J Baranyi

Publications and source records attributed to J Baranyi.

16 recordsLinked to original sources
PubMed2000

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.

Animals↗
PubMed2000

Predictive model for the growth of Yersinia enterocolitica under modified atmospheres.

A quadratic response surface model is presented to describe the maximum specific growth rate of Yersinia enterocolitica, at refrigeration temperatures, under modified atmospheres. The presence of CO2 affected mainly the lag phase of the organism. The length of the lag phase increased with higher levels of CO2 in the atmosphere, and this effect was more noticeable at low temperatures. The effect of oxygen was similar but less pronounced. The observed growth was slower with higher CO2. Oxygen also decreased the growth rate, but its effect was significant only when its proportion in the atmosphere was greater than about 40%. Model predictions were compared with growth rates obtained in sea food inoculated with Y. enterocolitica and packaged under modified atmospheres. Predictions were also checked to determine whether they were inside the strict interpolation region of the model.

Animals↗
PubMed1999

Validating and comparing predictive models.

The bias and accuracy factors introduced by Ross [Ross, T., 1996. Indices for performance evaluation of predictive models in food microbiology. J. Appl Bacteriol. 81, 501-508] for the evaluation of the performance of models in 'predictive food microbiology' are refined by basing the calculation of those measures on the mean square differences between predictions and observations. The use of the indices is extended by presenting formulae and methods which enable evaluation of the difference between alternative models for growth of an organism of interest over a domain of environmental factors. This is done by calculating the integral mean of the square differences between the models under investigation over the domain of the environmental variables common to those models, or a sub-region of it. The use of the techniques is exemplified by evaluating the difference between four published models for the growth rate of psychrotrophic pseudomonads.

Bias↗
PubMed1999

Predicting fungal growth: the effect of water activity on Penicillium roqueforti.

The effect of water activity on the colony growth of Penicillium roqueforti is studied by predictive modelling techniques. Measured colony diameter growth curves are fitted to estimate the growth rate and lag phase of the curves. The colony growth rate was modelled by a quadratic function of transformed water activity (a(w)) values, as suggested by Baranyi et al. (Food Microbiol. 10 (1993) 43-59). The lag time was modelled as a function of water activity, by means of the sum of a constant term and a hyperboloid function of a(w) raised to the second power. The lag-phase of Penicillium roqueforti was found insensitive to the water activity in the range of its higher (a(w) > 0.92) values.

Cheese↗
PubMed1999

Validating predictive models of food spoilage organisms.

The accuracy and bias of a predictive model for the maximum specific growth rate of Pseudomonas spp. were studied by means of percentage discrepancy and bias indicators. These were calculated for observations obtained both in laboratory media and in food. When independent pseudomonad data generated in broth were compared with model predictions, the error was smaller than in the case of food. The extent to which the food structure and composition of the microflora contribute to the overall error of the model was quantified.

Animals↗
PubMed1999

Estimating bacterial growth parameters by means of detection times.

We developed a new numerical method to estimate bacterial growth parameters by means of detection times generated by different initial counts. The observed detection times are subjected to a transformation involving the (unknown) maximum specific growth rate and the (known) ratios between the different inoculum sizes and the constant detectable level of counts. We present an analysis of variance (ANOVA) protocol based on a theoretical result according to which, if the specific rate used for the transformation is correct, the transformed values are scattered around the same mean irrespective of the original inoculum sizes. That mean, termed the physiological state of the inoculum, âlpha, and the maximum specific growth rate, mu, can be estimated by minimizing the variance ratio of the ANOVA procedure. The lag time of the population can be calculated as lambda = -ln âlpha/mu; i.e. the lag is inversely proportional to the maximum specific growth rate and depends on the initial physiological state of the population. The more accurately the cell number at the detection level is known, the better the estimate for the variance of the lag times of the individual cells.

Analysis of Variance↗
PubMed1998

Comparison of Stochastic and Deterministic Concepts of Bacterial Lag.

The shortcomings of the traditional deterministic approach to bacterial lag are exposed in this paper. A more precise, stochastic, formulation is put forward which takes account of the variation of the individual cell's lag time. Formulae are given to describe how the lag-distribution of the cells relates to the deterministic population lag commonly used as a practical measure of bacterial lag time.Copyright 1998 Academic Press Limited

Journal Article↗
PubMed1998

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.

Bacteria↗
PubMed1995

A response surface study on the role of some environmental factors affecting the growth of Saccharomyces cerevisiae.

The combined effect of pH, ethanol and fructose on the growth of Saccharomyces cerevisiae at 25 degrees C was studied by the standard Response Surface Methodology. Canonical analysis of the obtained response surface led to the conclusion that the effects of ethanol and fructose can be described by a single factor, the value of water activity, which can be calculated from the other two. Therefore the number of explanatory variables can be reduced. The computational usefulness of the transformation bw = square root of (1-aw) is demonstrated.

Ethanol↗
PubMed1995

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.

Bacteria↗
PubMed1995

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.

Food Microbiology↗
PubMed1994

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.

Bacteria↗
PubMed1994

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.

Aspergillus flavus↗
PubMed1994

Recovery of heat-injured Listeria monocytogenes.

Concern has been expressed that the ability of heat-injured Listeria monocytogenes cells to resuscitate during refrigerated storage of food may lead to underestimation of their heat resistance. The recovery of heat-injured L. monocytogenes was therefore examined as a function of incubation temperature and composition of recovery medium. Heat-injured cells exhibited a broad optimum temperature for recovery centered around 20-25 degrees C. The best recovery medium of those tested was blood agar. Incubation of cells in broth or chicken slurry at 5 degrees C (cold-enrichment) did not allow repair of potentially lethal injury i.e. it did not allow recovery of cells that would otherwise have died if incubated at a higher temperature. In some cases incubation of heat-injured cells at 5 degrees C resulted in death of a proportion of the population. Repair of sublethal heat-injury, measured as the time of incubation in tryptone soya broth needed to regain the ability to grow on Listeria selective agar, was slower and less complete at 25 degrees C than at 2 degrees C; repair took 10-15 h at 25 degrees C compared with 8-12 days at 5 degrees C. Refrigeration of heat-treated foods should not therefore increase the risk that heat-injured cells will recover from the heat treatment.

Colony Count, Microbial↗
PubMed1993

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.

Cell Division↗