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

Robinson, Tobin; Aboaba, O.O.; Kaloti, A.; Ocio, M.J.; Baranyi, József; Mackey, B.M.

doi:10.1016/s0168-1605(01)00541-4

Cited by 159 publications

(104 citation statements)

References 50 publications

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“…A challenge test in which the inoculum contains too many organisms may overstress the preservative system of a product which would not normally be at risk and lead to an overestimation of the risk, whereas too few organisms may give false negative results (Rose and Stringer, 1987). Studies have shown that beneath the common inoculation level of 1000 cfu/ml, the lag phase becomes longer and the probability of growth is less (Gay et al, 1996;Robinson et al, 2001;Pascual et al, 2001). Farber and Daley (1994) found that when L. monocytogenes was present in very low numbers on meats such as sliced ham, turkey breasts, wieners, and pâté stored at 4 jC, its numbers did not increase.…”

Section: Choice Of Inoculum Levelmentioning

confidence: 99%

Evaluation of a challenge testing protocol to assess the stability of ready-to-eat cooked meat products against growth of Listeria monocytogenes

Uyttendaele¹

2004

International Journal of Food Microbiology

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Challenge testing of ready-to-eat (RTE) foods with Listeria monocytogenes is recommended to assess the potential for growth. The present study was undertaken to evaluate a protocol for challenge testing applied to RTE cooked meat products. In order to choose L. monocytogenes strains with a representative behaviour, initially, the variability of the response of multiple L. monocytogenes strains of human and food origin to different stress and growth conditions was established. The strains were not inhibited in their growth at moderate acid pH (5.25) and the four strains tested in particular showed a similar acid-adaptive response. Growth of the various strains under four different combined stress conditions indicated that no L. monocytogenes strain had consistently significant longer or shorter lag phase or higher or lower maximum specific growth rates. The effect of choice of strain and history (pre-incubation temperature 7 or 30 jC) on growth of L. monocytogenes under optimum conditions (Brain Heart Infusion, BHI) and modified BHI simulating conditions of cooked ham and paté was studied. In general, all four L. monocytogenes strains behaved similarly. In BHI, no difference in lag phase was observed for the cold-adapted and standard inoculum, whereas in BHI adjusted to ham and pâté conditions, a ca. 40-h reduction of the lag phase was noted for the coldadapted inoculum. Subsequently, microbial challenge testing of L. monocytogenes in modified atmosphere packaged sliced cooked ham and paté was performed. A mixed inoculum of four L. monocytogenes strains and an inoculum level of ca. 1 -10 cfu/g was used. On vacuum packed sliced cooked ham, the concentration of 100 cfu/g, the safety limit considered as low risk for causing listeriosis, was exceeded after 5 days whereas ca. 10 5 cfu/g were obtained after 14 days when also LAB spoilers reached unacceptable numbers (ca. 10 7 cfu/g) whether standard or cold-adapted inoculum was used. The concentration of sodium lactate determined the opportunities for growth of L. monocytogenes in pâté. If growth of L. monocytogenes in pâté was noticed, the threshold of 100 cfu/ml was crossed earlier for the cold-adapted inoculum compared to the standard inoculum. D

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Section: Choice Of Inoculum Levelmentioning

confidence: 99%

Evaluation of a challenge testing protocol to assess the stability of ready-to-eat cooked meat products against growth of Listeria monocytogenes

Uyttendaele¹

2004

International Journal of Food Microbiology

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“…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%

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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“…However, most models for bacterial growth in food, as well as most experimental work in this area, are based on the use of relatively high inocula grown under homogeneous conditions (80). A more realistic scenario probably involves small numbers of contaminating bacteria that have been subjected to various physiological stresses such as starvation, heat injury, or osmotic shock (80,160,193,225). At these low levels of contamination, a single cell could give rise to a population that could ultimately cause spoilage or disease.…”

Section: Afterwordmentioning

confidence: 99%

“…Individually based modeling approaches, in conjunction with experimental evidence, can be useful in assessing these risks (130,157). Although this review has focused primarily on "high-tech" methods of single-cell analysis, much of the work done in bridging predictive modeling in food with experimental observation has been carried out using relatively "low-tech" tools such as turbidometry (80,160,193,225,255). In this approach, bacterial cultures are serially diluted to near extinction, yielding a high probability that individual wells of a microtiter plate will contain a single cell (80,225).…”

Section: Afterwordmentioning

confidence: 99%

Single-Cell Microbiology: Tools, Technologies, and Applications

Brehm-Stecher

Johnson

2004

Microbiol Mol Biol Rev

447

316

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The field of microbiology has traditionally been concerned with and focused on studies at the population level. Information on how cells respond to their environment, interact with each other, or undergo complex processes such as cellular differentiation or gene expression has been obtained mostly by inference from population-level data. Individual microorganisms, even those in supposedly “clonal” populations, may differ widely from each other in terms of their genetic composition, physiology, biochemistry, or behavior. This genetic and phenotypic heterogeneity has important practical consequences for a number of human interests, including antibiotic or biocide resistance, the productivity and stability of industrial fermentations, the efficacy of food preservatives, and the potential of pathogens to cause disease. New appreciation of the importance of cellular heterogeneity, coupled with recent advances in technology, has driven the development of new tools and techniques for the study of individual microbial cells. Because observations made at the single-cell level are not subject to the “averaging” effects characteristic of bulk-phase, population-level methods, they offer the unique capacity to observe discrete microbiological phenomena unavailable using traditional approaches. As a result, scientists have been able to characterize microorganisms, their activities, and their interactions at unprecedented levels of detail

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

Cited by 159 publications

References 50 publications

Evaluation of a challenge testing protocol to assess the stability of ready-to-eat cooked meat products against growth of Listeria monocytogenes

Evaluation of a challenge testing protocol to assess the stability of ready-to-eat cooked meat products against growth of Listeria monocytogenes

Influence of Stress on Individual Lag Time Distributions of Listeria monocytogenes

Single-Cell Microbiology: Tools, Technologies, and Applications

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