Estimation of uncertainty and variability in bacterial growth using Bayesian inference. Application to Listeria monocytogenes

Pouillot, Régis; Albert, Isabelle; Cornu, Marie; Denis, Jean-Baptiste

doi:10.1016/s0168-1605(02)00192-7

Cited by 109 publications

(62 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The dispersion was sometimes consistent with the biological variability obtained by other authors. Thus, Pouillot et al (2003) observed a mean SD of 1AE26°C for the biological variability of T min against an SD of 2AE55°C in our study. For the optimal specific growth rate of L. monocytogenes in milk, they observed a mean SD of 0AE18 h…”

Section: Discussionsupporting

confidence: 57%

“…To define confidence limits for predictions and to overpass the difficulties linked to the use of the classical bias and accuracy factors, we used the model parameter estimations distributions to estimate lower and upper confidence limits for the predicted growth rates. We assumed that the minimal cardinal values and the MICs were normally distributed and that the optimal specific growth rates followed a gamma distribution (Pouillot et al 2003). The distribution expectations were set to the mean of the observed values (Table 3), that was )1AE72°C for T min , 4AE26 for pH min HCl, 4AE71 for pH min lactic acid, 0AE913 for a w min , 25AE0 lmol l )1 for MIC nit , 31AE9 ppm for MIC phe , and 3AE04 for MIC CO 2 The distribution SDs were set according to the variability of the parameter estimations observed.…”

Section: Performance Characterization Of the Selected Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Growth rate and growth probability of Listeria monocytogenes in dairy, meat and seafood products in suboptimal conditions

et al. 2005

View full text Add to dashboard Cite

Aims: To evaluate the performances of models predicting the growth rate or the growth probability of Listeria monocytogenes in food. Methods and Results: Cardinal and square root type models including or not interactions between environmental factors and probability models were evaluated for their ability to describe the behaviour of L. monocytogenes in liquid dairy products, cheese, meat and seafood products. Models excluding interactions seemed sufficient to predict the growth rate of L. monocytogenes. However, the accurate prediction of growth/no-growth limits needed to take interactions into account. A complete and a simplified form (preservatives deducted) of a new cardinal model including interactions and parameter values were suggested to predict confidence limits for the growth rate of L. monocytogenes in food. This model could also be used for the growth probability prediction. Conclusions: The new cardinal model including interactions was efficient to predict confidence limits for the growth rate of L. monocytogenes and its growth probability in liquid dairy products, meat and seafood products. In cheese, the model was efficient to predict the absence of growth of the pathogen. Significance and Impact of the Study: The suggested model can be used for risk assessment and risk management concerning L. monocytogenes in dairy, meat and seafood products.

show abstract

Section: Discussionsupporting

confidence: 57%

Section: Performance Characterization Of the Selected Modelsmentioning

confidence: 99%

Growth rate and growth probability of Listeria monocytogenes in dairy, meat and seafood products in suboptimal conditions

et al. 2005

View full text Add to dashboard Cite

show abstract

“…In order to take into account the strain variability in the latter model, a normal distribution N(Ϫ2.47, 1.26) was used for T min , as proposed by Pouillot et al (43). The lag time at a certain temperature was calculated based on the physiological state parameter h 0 .…”

Section: Methodsmentioning

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

135

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

“…In these models, hyperprior distributions are assigned to hyperparameters. Through the hyperprior distributions, hierarchical Bayesian models account for the variability and the uncertainty of the parameters of interest (23,58,61).The two statistical methods were applied to the estimation of the contamination of vegetables with the food-borne pathogenic bacterium Listeria monocytogenes. L. monocytogenes has caused several recognized outbreaks of food-borne infections in which vegetables were implicated; it is regularly isolated from fresh vegetables and can survive and grow even at low temperatures (7,8,34,54,65).…”

mentioning

confidence: 99%

mentioning

confidence: 99%

Estimation of Microbial Contamination of Food from Prevalence and Concentration Data: Application to Listeria monocytogenes in Fresh Vegetables

Crépet¹,

Albert²,

Dervin³

et al. 2007

Appl Environ Microbiol

View full text Add to dashboard Cite

A normal distribution and a mixture model of two normal distributions in a Bayesian approach using prevalence and concentration data were used to establish the distribution of contamination of the food-borne pathogenic bacteria Listeria monocytogenes in unprocessed and minimally processed fresh vegetables. A total of 165 prevalence studies, including 15 studies with concentration data, were taken from the scientific literature and from technical reports and used for statistical analysis. The predicted mean of the normal distribution of the logarithms of viable L. monocytogenes per gram of fresh vegetables was ؊2.63 log viable L. monocytogenes organisms/g, and its standard deviation was 1.48 log viable L. monocytogenes organisms/g. These values were determined by considering one contaminated sample in prevalence studies in which samples are in fact negative. This deliberate overestimation is necessary to complete calculations. With the mixture model, the predicted mean of the distribution of the logarithm of viable L. monocytogenes per gram of fresh vegetables was ؊3.38 log viable L. monocytogenes organisms/g and its standard deviation was 1.46 log viable L. monocytogenes organisms/g. The probabilities of fresh unprocessed and minimally processed vegetables being contaminated with concentrations higher than 1, 2, and 3 log viable L. monocytogenes organisms/g were 1.44, 0.63, and 0.17%, respectively. Introducing a sensitivity rate of 80 or 95% in the mixture model had a small effect on the estimation of the contamination. In contrast, introducing a low sensitivity rate (40%) resulted in marked differences, especially for high percentiles. There was a significantly lower estimation of contamination in the papers and reports of 2000 to 2005 than in those of 1988 to 1999 and a lower estimation of contamination of leafy salads than that of sprouts and other vegetables. The interest of the mixture model for the estimation of microbial contamination is discussed.Quantitative microbial risk assessment (QMRA) is in rapid development in the area of food safety. To obtain a quantitative exposure assessment or a quantitative risk characterization for a given food and a given food-borne pathogen, statistical distributions of microbial concentrations are used as input values (such as bacterial concentrations in raw food materials subjected to further process) and/or as output values (predicted contamination in foods after processing and/or storage) (35,75). Microbial contaminations in foods are expressed in two forms: (i) prevalence data (percentage of positive samples in a given study, i.e., growth/no growth of a target pathogen after enrichment in an appropriate broth of an aliquot of a food sample) and (ii) concentration data expressed as CFU per gram or CFU per milliliter. Data on the concentration of pathogenic bacteria in foods, while scarce, are essential to QMRA.Previous works on QMRA did not consider low levels of concentrations, i.e., concentrations below the threshold of detection of microbiological methods (6,15,44...

show abstract

Estimation of uncertainty and variability in bacterial growth using Bayesian inference. Application to Listeria monocytogenes

Cited by 109 publications

References 56 publications

Growth rate and growth probability of Listeria monocytogenes in dairy, meat and seafood products in suboptimal conditions

Growth rate and growth probability of Listeria monocytogenes in dairy, meat and seafood products in suboptimal conditions

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

Estimation of Microbial Contamination of Food from Prevalence and Concentration Data: Application to Listeria monocytogenes in Fresh Vegetables

Contact Info

Product

Resources

About