Predictive modelling of growth of Listeria monocytogenes The effects on growth of NaCl, pH, storage temperature and NaNO2

McClure, Peter; Beaumont, A.L.; Sutherland, Jane P.; Roberts, T. A.

doi:10.1016/s0168-1605(96)01193-2

Cited by 126 publications

(57 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The most important factors are pH, a w , NaCl, sodium nitrite, sodium lactate, atmosphere, length and temperature of storage (ICMSF, 1996;Schott and Hildebrandt, 1996). Predictive models have been developed including some of these factors (McClure et al, 1997;Nerbrink et al, 1999;Devlieghere et al, 2001) and may be helpful to describe growth of L. monocytogenes in at risk foods. However, other factors should also be considered such as strain type (Begot et al, 1997), inoculum size , history (Gay et al, 1996;, interactions with competitive flora, e.g.…”

Section: Growth Potential Of L Monocytogenes In Rte Cooked Meat Prodmentioning

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

View full text Add to dashboard Cite

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

show abstract

Section: Growth Potential Of L Monocytogenes In Rte Cooked Meat Prodmentioning

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

View full text Add to dashboard Cite

show abstract

“…The Baranyi model (2, 3) is based on four parameters: a parameter expressing the lag phase, which in the case of inactivation curves will be regarded as the shoulder period, or survival period (SP); DR, the death rate (log 10 CFU per gram per day); y 0 , representing the upper asymptote, which corresponds to the initial bacterial counts (log 10 CFU per gram; and y end , representing the lower asymptote, which corresponds to final bacterial counts (log 10 CFU per gram). Using response surface methodology (6,9,17,24), these parameters can be further expressed as a quadratic function of temperature, pH, and essential oil concentration using the following equation:…”

Section: Vol 66 2000 Survival Of E Coli O157:h7 In Eggplant Salad mentioning

confidence: 99%

Development and Evaluation of a Model Predicting the Survival of Escherichia coli O157:H7 NCTC 12900 in Homemade Eggplant Salad at Various Temperatures, pHs, and Oregano Essential Oil Concentrations

Skandamis

Nychas

2000

Appl Environ Microbiol

203

103

View full text Add to dashboard Cite

Homemade eggplant salad, a traditional Greek appetizer, was inoculated with Escherichia coli O157:H7 NCTC 12900 supplemented with different concentrations of oregano essential oil (0.0, 0.7, 1.4, and 2.1% [vol/wt]) and stored at different temperatures (0, 5, 10, and 15°C). The product's pH was adjusted to 4.0, 4.5, or 5.0 with lemon juice. For each combination of the environmental factors, the bacterial counts were modeled, using the Baranyi model, as a function of time to estimate the kinetic parameters of the pathogen. A reduction of more than 1 log unit in E. coli O157:H7 counts was observed in all cases, and the death rate depended on the pH, the storage temperature, and the essential oil concentration. Separate quadratic models were developed with natural logarithms of the shoulder period and death rate as estimated by the growth model, as a function of temperature, pH, and oregano essential oil concentrations. These were further used to predict the population of E. coli O157:H7 NCTC 12900 from other inoculated eggplant salads at random conditions of temperature, pH, and oregano oil concentration. The predicted values were compared with viable-count measurements for validation.

show abstract

“…During the last decade, a large number of mathematical models for L. monocytogenes growth have been published (9,11,16,19,21,24,26,31,38), and some of them have been targeted to pasteurized milk (1,40,49). However, since the available data show that conditions that prevail during the chill chain vary significantly (8,17,23,27,28,34,44,45,48), the value of a deterministic application of these models as a tool in safety management of pasteurized milk would be limited.…”

mentioning

confidence: 99%

Probabilistic Model for Listeria monocytogenes Growth during Distribution, Retail Storage, and Domestic Storage of Pasteurized Milk

Koutsoumanis¹,

Pavlis²,

Nychas

et al. 2010

Appl Environ Microbiol

134

View full text Add to dashboard Cite

A survey on the time-temperature conditions of pasteurized milk in Greece during transportation to retail, retail storage, and domestic storage and handling was performed. The data derived from the survey were described with appropriate probability distributions and introduced into a growth model of Listeria monocytogenes in pasteurized milk which was appropriately modified for taking into account strain variability. Based on the above components, a probabilistic model was applied to evaluate the growth of L. monocytogenes during the chill chain of pasteurized milk using a Monte Carlo simulation. The model predicted that, in 44.8% of the milk cartons released in the market, the pathogen will grow until the time of consumption. For these products the estimated mean total growth of L. monocytogenes during transportation, retail storage, and domestic storage was 0.93 log CFU, with 95th and 99th percentiles of 2.68 and 4.01 log CFU, respectively. Although based on EU regulation 2073/2005 pasteurized milk produced in Greece belongs to the category of products that do not allow the growth of L. monocytogenes due to a shelf life (defined by law) of 5 days, the above results show that this shelf life limit cannot prevent L. monocytogenes from growing under the current chill chain conditions. The predicted percentage of milk cartons-initially contaminated with 1 cell/1-liter carton-in which the pathogen exceeds the safety criterion of 100 cells/ml at the time of consumption was 0.14%. The probabilistic model was used for an importance analysis of the chill chain factors, using rank order correlation, while selected intervention and shelf life increase scenarios were evaluated. The results showed that simple interventions, such as excluding the door shelf from the domestic storage of pasteurized milk, can effectively reduce the growth of the pathogen. The door shelf was found to be the warmest position in domestic refrigerators, and it was most frequently used by the consumers for domestic storage of pasteurized milk. Furthermore, the model predicted that a combination of this intervention with a decrease of the mean temperature of domestic refrigerators by 2°C may allow an extension of pasteurized milk shelf life from 5 to 7 days without affecting the current consumer exposure to L. monocytogenes.

show abstract

Predictive modelling of growth of Listeria monocytogenes The effects on growth of NaCl, pH, storage temperature and NaNO2

Cited by 126 publications

References 45 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

Development and Evaluation of a Model Predicting the Survival of Escherichia coli O157:H7 NCTC 12900 in Homemade Eggplant Salad at Various Temperatures, pHs, and Oregano Essential Oil Concentrations

Probabilistic Model for Listeria monocytogenes Growth during Distribution, Retail Storage, and Domestic Storage of Pasteurized Milk

Contact Info

Product

Resources

About