Effect of environmental parameters (temperature, pH and a(w)) on the individual cell lag phase and generation time of Listeria monocytogenes. 2006

K Francois, and F Devlieghere, and A R Standaert, and A H Geeraerd, and J F Van Impe, and J Debevere
Faculty of Bioscience Engineering, Laboratory of Food Microbiology and Food Preservation, Department of Food Safety and Food Quality, Ghent University, Coupure Links 653, B-9000 Ghent, Belgium.

The effect of the individual environmental factors temperature (2-30 degrees C), pH (4.4-7.4) and a(w) (0.947-0.995) as well as the combinations of these factors on the individual cell lag phase and the generation time of Listeria monocytogenes was investigated. Individual cells were isolated using a serial dilution protocol in microtiter plates, and subsequent growth was investigated by optical density (OD) measurements at 600 nm. About 100 replicates were made for each set of environmental conditions. Part of the data were previously published in Francois et al. (Francois, K., Devlieghere, F., Smet, K., Standaert, A.R., Geeraerd, A.H., Van Impe, J.F., Debevere, J., 2005a. Modelling the individual cell lag phase: effect of temperature and pH on the individual cell lag distribution of Listeria monocytogenes. Int. J. Food Microbiol. 100, 41-53.), but were recalculated here using the calibration curves for transformation of optical density to colony forming units/ml from Francois et al. (Francois, K., Devlieghere, F., Standaert, A.R., Geeraerd, A.H., Cools, I., Van Impe, J.F., Debevere, J., 2005b. Environmental factors influencing the relationship between optical density and cell count for Listeria monocytogenes. J. Appl. Microbiol. 99, 1503-1515), as this calibration curve appeared to be dependent on the environmental parameters. The previous dataset was also extended with a factor a(w), observed individually and combinations with the above mentioned environmental factors. Individual cell lag phases and subsequent growth rates were calculated assuming an exponential growth model. The results are discussed as mean values to determine the general trends and in addition, histograms are made and statistical distributions are fitted to the different data sets. When stress levels increased, the mean values and the variability observed for the individual cell lag phases increased, resulting in broader histograms and distributions that were shifting to the right. Also the gravity point of the distributions was shifting from a skewed left type to a more symmetrical type. The best description of the data is obtained with an exponential distribution for low stress levels, a gamma distribution for intermediate stress and a Weibull distribution for severe stress levels. When only low stress levels were applied, a significant percentage of the cells showed no lag phase. In those cases, a new approach was used to obtain better fits: cells with a lag phase and those without a lag phase were separated using a binomial distribution while in a second step, a gamma or a Weibull distribution is fitted to the fraction of cells showing a lag phase. A normal distribution is used to describe the variability of the generation times. These distributions can be applied to refine the exposure assessment part of the risk assessment concerning L. monocytogenes by incorporating intercellular variability.

UI MeSH Term Description Entries
D007700 Kinetics The rate dynamics in chemical or physical systems.
D008089 Listeria monocytogenes A species of gram-positive, rod-shaped bacteria widely distributed in nature. It has been isolated from sewage, soil, silage, and from feces of healthy animals and man. Infection with this bacterium leads to encephalitis, meningitis, endocarditis, and abortion.
D008954 Models, Biological Theoretical representations that simulate the behavior or activity of biological processes or diseases. For disease models in living animals, DISEASE MODELS, ANIMAL is available. Biological models include the use of mathematical equations, computers, and other electronic equipment. Biological Model,Biological Models,Model, Biological,Models, Biologic,Biologic Model,Biologic Models,Model, Biologic
D011237 Predictive Value of Tests In screening and diagnostic tests, the probability that a person with a positive test is a true positive (i.e., has the disease), is referred to as the predictive value of a positive test; whereas, the predictive value of a negative test is the probability that the person with a negative test does not have the disease. Predictive value is related to the sensitivity and specificity of the test. Negative Predictive Value,Positive Predictive Value,Predictive Value Of Test,Predictive Values Of Tests,Negative Predictive Values,Positive Predictive Values,Predictive Value, Negative,Predictive Value, Positive
D005516 Food Microbiology The presence of bacteria, viruses, and fungi in food and food products. This term is not restricted to pathogenic organisms: the presence of various non-pathogenic bacteria and fungi in cheeses and wines, for example, is included in this concept. Microbiology, Food
D006863 Hydrogen-Ion Concentration The normality of a solution with respect to HYDROGEN ions; H+. It is related to acidity measurements in most cases by pH pH,Concentration, Hydrogen-Ion,Concentrations, Hydrogen-Ion,Hydrogen Ion Concentration,Hydrogen-Ion Concentrations
D013696 Temperature The property of objects that determines the direction of heat flow when they are placed in direct thermal contact. The temperature is the energy of microscopic motions (vibrational and translational) of the particles of atoms. Temperatures
D014867 Water A clear, odorless, tasteless liquid that is essential for most animal and plant life and is an excellent solvent for many substances. The chemical formula is hydrogen oxide (H2O). (McGraw-Hill Dictionary of Scientific and Technical Terms, 4th ed) Hydrogen Oxide
D015169 Colony Count, Microbial Enumeration by direct count of viable, isolated bacterial, archaeal, or fungal CELLS or SPORES capable of growth on solid CULTURE MEDIA. The method is used routinely by environmental microbiologists for quantifying organisms in AIR; FOOD; and WATER; by clinicians for measuring patients' microbial load; and in antimicrobial drug testing. Agar Dilution Count,Colony-Forming Units Assay, Microbial,Fungal Count,Pour Plate Count,Spore Count,Spread Plate Count,Streak Plate Count,Colony Forming Units Assay, Microbial,Colony Forming Units Assays, Microbial,Agar Dilution Counts,Colony Counts, Microbial,Count, Agar Dilution,Count, Fungal,Count, Microbial Colony,Count, Pour Plate,Count, Spore,Count, Spread Plate,Count, Streak Plate,Counts, Agar Dilution,Counts, Fungal,Counts, Microbial Colony,Counts, Pour Plate,Counts, Spore,Counts, Spread Plate,Counts, Streak Plate,Dilution Count, Agar,Dilution Counts, Agar,Fungal Counts,Microbial Colony Count,Microbial Colony Counts,Pour Plate Counts,Spore Counts,Spread Plate Counts,Streak Plate Counts
D015233 Models, Statistical Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc. Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model

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