Modelling the effect of sublethal injury on the distribution of the lag times of individual cells of Lactobacillus plantarum

Smelt, J.P.P.M.; Otten, G.D.; Bos, Ad P.

doi:10.1016/s0168-1605(01)00651-1

Cited by 83 publications

(56 citation statements)

References 12 publications

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“…A small number of previous studies have sought to examine this phenomenon of the historical impact of stress on individual lag times; furthermore, they have essentially concerned one stress (4,33,34). By using a similar physiological parameter, the loss of cultivability, for each stress, we succeeded in classifying the impacts of nine different stresses on the individual lag time distributions.…”

Section: Discussionmentioning

confidence: 99%

“…Similarly, the individual response was also tackled by some authors such as Baranyi (6), who showed the relationship between the individual lag time distributions and the lag time of the bacterial population. Recently, some individual lag time distributions have been characterized (23,33). The variability of individual lag times seems to be wider as microorganisms are injured (4,33,34,39) or as growth conditions are unfavorable (23,24,27).…”

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confidence: 99%

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Influence of Stress on Individual Lag Time Distributions of Listeria monocytogenes

Guillier

Pardon

Augustin

2005

Appl Environ Microbiol

114

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The effects of nine common food industry stresses on the times to the turbidity (T d ) distribution of Listeria monocytogenes were determined. It was established that the main source of the variability of T d for stressed cells was the variability of individual lag times. The distributions of T d revealed that there was a noticeable difference in response to the stresses encountered by the L. monocytogenes cells. The applied stresses led to significant changes of the shape, the mean, and the variance of the distributions. The variance of T d of wells inoculated with single cells issued from a culture in the exponential growth phase was multiplied by at least 6 and up to 355 for wells inoculated with stressed cells. These results suggest stress-induced variability may be important in determining the reliability of predictive microbiological models.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

Influence of Stress on Individual Lag Time Distributions of Listeria monocytogenes

Guillier

Pardon

Augustin

2005

Appl Environ Microbiol

114

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“…It has indeed been shown that lag times can vary widely between individual cells in a population, and the inherent variability in the lag time of single cells increases with severity of heat treatment (7,15,27,28). Knowing how heat treatments affect the variability of single-cell lag times is extremely important in assessing the risk of cell recovery and growth in processed foods where low numbers of stressed cells of pathogenic bacteria may be distributed among different packs of food.…”

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confidence: 99%

Modeling the Variability of Single-Cell Lag Times for Listeria innocua Populations after Sublethal and Lethal Heat Treatments

Métris

George

Mackey

et al. 2008

Appl Environ Microbiol

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Optical density measurements were used to estimate the effect of heat treatments on the single-cell lag times of Listeria innocua fitted to a shifted gamma distribution. The single-cell lag time was subdivided into repair time (the shift of the distribution assumed to be uniform for all cells) and adjustment time (varying randomly from cell to cell). After heat treatments in which all of the cells recovered (sublethal), the repair time and the mean and the variance of the single-cell adjustment time increased with the severity of the treatment. When the heat treatments resulted in a loss of viability (lethal), the repair time of the survivors increased with the decimal reduction of the cell numbers independently of the temperature, while the mean and variance of the single-cell adjustment times remained the same irrespective of the heat treatment. Based on these observations and modeling of the effect of time and temperature of the heat treatment, we propose that the severity of a heat treatment can be characterized by the repair time of the cells whether the heat treatment is lethal or not, an extension of the F value concept for sublethal heat treatments. In addition, the repair time could be interpreted as the extent or degree of injury with a multiple-hit lethality model. Another implication of these results is that the distribution of the time for cells to reach unacceptable numbers in food is not affected by the timetemperature combination resulting in a given decimal reduction.Heat treatment is a method of preservation widely used in the food industry (24). Relatively mild thermal treatments below 100°C are sufficient to kill vegetative cells of food-borne pathogens or spoilage organisms, while inactivation of bacterial spores in nonacid foods requires much higher process temperatures, typically 121°C or more. In early studies to develop processes for the safe production of canned foods, the concept of thermal death point was used, this being defined as the length of time at different temperatures needed to destroy a definite concentration of spores under defined conditions (3). The work of Esty and Meyer (8) demonstrated a linear relationship between heating temperature and the logarithm of the time needed to inactivate suspensions containing 60 billion spores of proteolytic Clostridium botulinum. This work, often cited as the first example of a predictive model, led to the standard for canned food sterilization being based on a reduction of the spore population by a factor of 10 12 . Studies on the kinetics of thermal inactivation established that the concentration of viable vegetative cells or spores decreases more or less exponentially with time of heating such that a plot of the logarithm of the viable cell concentration versus time yields a straight line (3, 6). The assumption of a logarithmic order of death underlies thermal process calculations based on D and z values, where D is the time needed for a 10-fold reduction in viable numbers and z is the temperature change needed to bring about a 10-fo...

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“…Measuring the lag time of individual cells requires direct microscopic observation [4,8] or techniques to isolate single cells [10]. Cell isolation can be achieved by diluting [2], sorting by flow cytometry [11] or inactivating all organisms except one [9]. When growth is detected in some samples and not in others, it is commonly assumed that growth comes from one cell [12].…”

Section: Introductionmentioning

confidence: 99%

“…When a certain percentage of samples does not show growth, the assumption that growth in the other samples is due to one cell contravenes the predictions of the Poisson distribution. Several researchers have used the Poisson distribution to calculate the proportion of growthpositive samples initially containing more than one cell [5,11,15,16]. McKellar and Hawke [17] recognised that one of the limitations of the Bioscreen as a tool to study single cell behaviour is that it is difficult to ensure that the growth in any given positive well arose from a single cell.…”

Section: Introductionmentioning

confidence: 99%

The Poisson Distribution Is Applied to Improve the Estimation of Individual Cell and Micropopulation Lag Phases

Aguirre¹,

Bravo²,

Ordóñez³

et al. 2012

AiM

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Many articles dealing with individual cell lag phase determination assume that growth, when observed, comes from one cell. This assumption is not in agreement with the Poisson distribution, which uses the probability of growth in a sample to predict how many samples contain one, two, or some other number of cells. This article analyses and compares different approaches to improve the accuracy of lag phase estimation of individual cells and micropopulations. It argues that if the highest initial load, as predicted by the Poisson distribution, is assigned to the sample with the shortest lag phase, the second highest to the sample with the second shortest lag phase and so on, the resulting lag phase distributions would be more accurate. This study also proposes the use of a robust test, permutation test, to compare lag phase distributions obtained in different situations.

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Modelling the effect of sublethal injury on the distribution of the lag times of individual cells of Lactobacillus plantarum

Cited by 83 publications

References 12 publications

Influence of Stress on Individual Lag Time Distributions of Listeria monocytogenes

Influence of Stress on Individual Lag Time Distributions of Listeria monocytogenes

Modeling the Variability of Single-Cell Lag Times for Listeria innocua Populations after Sublethal and Lethal Heat Treatments

The Poisson Distribution Is Applied to Improve the Estimation of Individual Cell and Micropopulation Lag Phases

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