Use of sensitivity analysis to aid interpretation of a probabilistic Bacillus cereus spore lag time model applied to heat-treated chilled foods (REPFEDs)

Membré, Jeanne‐Marie; Kan-King-Yu, Denis; Blackburn, C. de W.

doi:10.1016/j.ijfoodmicro.2008.06.029

Cited by 17 publications

(10 citation statements)

References 27 publications

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“…A number of studies investigated the growth characteristics of B. cereus under different environmental conditions (8,11,14,16,22,32). However, there has been no investigation on the development of a predictive model for B. cereus in cooked vegetables.…”

Section: Resultsmentioning

confidence: 99%

Growth and predictive model of Bacillus cereus on blanched spinach with or without seasoning at various temperatures

Bae

Kim

Lee

et al. 2012

Food Sci Biotechnol

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The growth of Bacillus cereus on blanched spinach with or without seasoning at various temperatures (15,20,25,30, and 35 o C) was investigated. The number of B. cereus on blanched spinach stored at 35 o C was significantly increased and resulted in maximum populations (7.8 log CFU/g) after 10 h. However, the growth rate of B. cereus on blanched spinach with seasoning stored at 35 o C was lower than on blanched spinach without seasoning. The growth rate (GR) of B. cereus on blanched spinach stored at 15 o C was lower than the other temperatures. The lag time (LT) of B. cereus on blanched spinach with seasoning was longer than blanched spinach without seasoning. Primary growth models of B. cereus on blanched spinach fit well to a modified Gompertz model (blanched spinach R 2 =0.98; blanched spinach with seasoning R 2 =0.96). These results might be used for developing safe storage guidelines for cooked vegetable at various temperatures.

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Section: Resultsmentioning

confidence: 99%

Growth and predictive model of Bacillus cereus on blanched spinach with or without seasoning at various temperatures

Bae

Kim

Lee

et al. 2012

Food Sci Biotechnol

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“…Likewise, having uncertainty and variability separated is helpful in mitigation risk strategies: among several options tested by scenario analysis, only the ones which lead to a significant risk reduction are kept for potential risk reduction measures recommended to risk managers. As stressed by Membré et al (2008), reporting the uncertainty impact on the model output through the use of confidence interval bars, facilitates the interpretation and then the communication. Greater transparency and better understanding provide for better confidence in the model output when making food safety decisions.…”

Section: Advantage Of Adding the Uncertainty Dimension To A Qmea Modelmentioning

confidence: 99%

Added value of experts' knowledge to improve a quantitative microbial exposure assessment model — Application to aseptic-UHT food products

Pujol

Johnson

Magras

et al. 2015

International Journal of Food Microbiology

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“…This approach, initially developed in association with an ANOVA by Mokhtari and Frey (2005) and further applied by Pouillot et al (2007) and Membré et al (2008), was here used with a computation of Sobol indices. The model response was G. stearothermophilus concentration after sterilization, C STERI (in log 10 CFU/g).…”

Section: Sensitivity Analysismentioning

confidence: 99%

“…Sensitivity analysis is increasingly applied to risk assessment with one-dimension (Zwietering and Van Gerwen, 2000;Frey et al, 2003;Mokhtari and Frey, 2005;Ellouze et al, 2010) or two-dimension models (Pouillot et al, 2007;Membré et al, 2008;Mataragas et al, 2010;Busschaert et al, 2011). Sensitivity analysis or what-if-scenarios may be used to identify the highest sources of variability and of uncertainty in the final model.…”

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confidence: 99%

Quantitative assessment of the risk of microbial spoilage in foods. Prediction of non-stability at 55°C caused by Geobacillus stearothermophilus in canned green beans

Rigaux¹,

André

Albert³

et al. 2014

International Journal of Food Microbiology

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Microbial spoilage of canned foods by thermophilic and highly heat-resistant spore-forming bacteria, such as Geobacillus stearothermophilus, is a persistent problem in the food industry. An incubation test at 55°C for 7 days, then validation of biological stability, is used as an indicator of compliance with good manufacturing practices. We propose a microbial risk assessment model predicting the percentage of non-stability due to G. stearothermophilus in canned green beans manufactured by a French company. The model accounts for initial microbial contaminations of fresh unprocessed green beans with G. stearothermophilus, cross-contaminations in the processing chain, inactivation processes and probability of survival and growth. The sterilization process is modeled by an equivalent heating time depending on sterilization value F 0 and on G. stearothermophilus resistance parameter z T . Following the recommendations of international organizations, second order MonteCarlo simulations are used, separately propagating uncertainty and variability on parameters. As a result of the model, the mean predicted non-stability rate is of 0.5%, with a 95% uncertainty interval of [0.1%; 1.2%], which is highly similar to data communicated by the French industry. A sensitivity analysis based on Sobol indices and some scenario tests underline the importance of cross-contamination at the blanching step, in addition to inactivation due to the sterilization process.

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Use of sensitivity analysis to aid interpretation of a probabilistic Bacillus cereus spore lag time model applied to heat-treated chilled foods (REPFEDs)

Cited by 17 publications

References 27 publications

Growth and predictive model of Bacillus cereus on blanched spinach with or without seasoning at various temperatures

Growth and predictive model of Bacillus cereus on blanched spinach with or without seasoning at various temperatures

Added value of experts' knowledge to improve a quantitative microbial exposure assessment model — Application to aseptic-UHT food products

Quantitative assessment of the risk of microbial spoilage in foods. Prediction of non-stability at 55°C caused by Geobacillus stearothermophilus in canned green beans

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