Influence of Stress on Individual Lag Time Distributions of Listeria monocytogenes

Abstract

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.

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

“…It is recommended that approximately 100 samples show growth [13], and this figure must not be a high percentage of the samples. Guillier et al [5] stated that if 35% of samples show growth, this should not significantly affect individual cell lag phase distributions because at least 80% of samples contain one cell, according to the Poisson distribution function.…”

“…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.…”

“…However, the lag phase of populations composed by few cells, or even by only individual cells, is inherently variable. Therefore, it is understandable that researchers [1][2][3][4][5][6][7][8][9] have paid attention to the distribution of single cells lag times and to the techniques that can measure them. Measuring the lag time of individual cells requires direct microscopic observation [4,8] or techniques to isolate single cells [10].…”

Improvement of methods for the detection of Gram-negative foodborne pathogens

“…When 141 different powdered infant formula originating from 35 countries were examined in 1988, 20 samples contained Cronobacter spp. in a range from 0.36 to 66 cfu per 100g (45). More recent data show that levels have decreased considerably to -3.84± 0.696 log cfu/g (46).…”

“…With the TTD, the growth rate and the number of cfu at the time to detection, the lag time can be calculated. Although this method is easy and rapid and is described by several authors (44,45), certain limitations should be pointed out. The main problem when using OD is the limited range of validity resulting from a high detection limit of the method (10 6 -10 7 cfu/ml) (46).…”