Meta-analysis for quantitative microbiological risk assessments and benchmarking data

Trends in Food Science and Technology

Volumn 25, Issue 1, 2012, Pages 34-39

  den Besten, Heidy M W ^a   Zwietering, Marcel H ^a  

^a WAGENINGEN UNIVERSITY   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARKING DATA; GENERIC PARAMETERS; GLOBAL ESTIMATE; INFLUENCING FACTOR; META-ANALYSIS; QUANTITATIVE MICROBIOLOGICAL RISK ASSESSMENTS;

RISK ASSESSMENT;

FACTOR ANALYSIS;

EID: 84860516850     PISSN: 09242244     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.tifs.2011.12.004     Document Type: Review

References (26)

1. Baranyi J., Tamplin M.L. ComBase: a common database on microbial responses to food environments. Journal of Food Protection 2004, 67:1967-1971.
2. Den Aantrekker E.D., Beumer R.R., van Gerwen S.J.C., Zwietering M.H., van Schothorst M., et al. Estimating the probability of recontamination via the air using Monte Carlo simulations. International Journal of Food Microbiology 2003, 87:1-15.
3. FAO/WHO Application of risk analysis to food standards issues: Report of a joint FAO/WHO expert consultation 1995, WHO/FNU/FOS/95.3.
4. FAO/WHO Enterobacter sakazakii and Salmonella in powdered infant formula: meeting report. Microbiological risk assessment series no.10 2006, FAO/WHO, Rome. S. Cahill (Ed.).
5. Gonzales-Barron U., Butler F. The use of meta-analytical tools in risk assessment for food safety. Food Microbiology 2011, 28:823-827.
6. Halder A., Black D.G., Davidson P.M., Datta A. Development of associations and kinetic models for microbiological data to be used in comprehensive food safety prediction software. Journal of Food Science 2010, 75:R107-R120.
7. Halvorsen K.T. Combining results from independent investigations: meta-analysis in medical research. Medical uses of statistics 1986, 392-416. Massachusetts: NEJM Books. J.C. Bailar, F. Mosteller (Eds.).
8. Lammerding A.M. An overview of microbial food safety risk assessment. Journal of Food Protection 1997, 60:1420-1425.
9. Lammerding A.M., Paoli G.M. Quantitative risk assessment: an emerging tool for emerging foodborne pathogens. Emerging Infectious Diseases 1997, 3:483-487.
10. Leporq B., Membré J.M., Dervin C., Buche P., Guyonnet J.P. The " Sym'Previus" software, a tool to support decisions to the foodstuff safety. International Journal of Food Microbiology 2005, 100:231-237.
11. Ma L., Zhang G., Gerner-Smidt P., Mantripragada V., Ezeoke I., Doyle M.P. Thermal inactivation of Salmonella in peanut butter. Journal of Food Protection 2009, 72:1596-1601.
12. McMeekin T.A. Predictive microbiology: quantitative science delivering quantifiable benefits to the meat industry and other food industries. Meat Science 2007, 77:17-27.
13. McMeekin T., Tamplin M., Ross T., Koseki S. The way forward with predictive microbiology in the dairy industry. The Australian Journal of Dairy Technology 2010, 65:91-97.
14. McQuestin O.J., Shadbolt C.T., Ross T. Quantification of the relative effects of temperature, pH, and water activity on inactivation of Escherichia coli in fermented meat by meta-analysis. Applied Environmental Microbiology 2009, 75:6963-6972.
15. Muñoz-Cuevas M., Metris A., Baranyi J. Predictive modelling of Salmonella: from cell cycle measurements to e-models. Food Research International 2011, 10.1016/j.foodres.2011.04.033.
16. Pérez-Rodríguez F., van Asselt E.D., García-Gimeno R.M., Zurera G., Zwietering M.H. Extracting additional risk managers information from a risk assessment of Listeria monocytogens in deli meats. Joural of Food Protection 2007, 70:1137-1152.
17. Ratkowsky D.A., Olley J., McMeekin T.A., Ball A. Relationship between temperature and growth rate of bacterial cultures. Journal of Bacteriology 1982, 149:1-5.
18. Rodricks J.V. Risk assessment, the environment, and public health. Environmental Health Perspectives 1994, 102:258-264.
19. Ross T., Zhang D., McQuestin O.J. Temperature governs the inactivation rate of vegetative bacteria under growth-preventing conditions. International Journal of Food Microbiology 2008, 128:129-135.
20. Santillana Farakos S.M., Zwietering M.H. Data analysis of the inactivation of food-borne microorganisms under high hydrostatic pressure to establish global kinetic parameters and influencing factors. Journal of Food Protection 2011, 74:2097-2106.
21. Valdramidis V.P., Geeraerd A.H., Tiwari B.K., Cullen P.J., Kondjoyan A., Van Impe J.F. Estimating the efficacy of mild heating processes taking into account microbial non-linearities: a case study on the thermisation of a food simulant. Food Control 2011, 22:137-142.
22. Van Asselt E.D., Zwietering M.H. A systematic approach to determine global thermal inactivation parameters for various food pathogens. International Journal of Food Microbiology 2006, 107:73-82.
23. 10 for bacteria and spores under various conditions. Journal of Food Protection 1999, 62:1024-1032.
24. Vialette M., Pinon A., Leporq B., Dervin C., Membré J.-M. Meta-analysis of food safety information based on a combination of a relational database and a predictive modeling tool. Risk Analysis 2005, 25:75-83.
25. Whitehead A. Meta-analysis of controlled clinical trials 2002, John Wiley & Sons Ltd, West Sussex.
26. Xanthiakos K., Simos D., Angelidis A.S., Nychas G.J.E., Koutsoumanis K. Dynamic modeling of Listeria monocytogenes growth in pasteurized milk. Journal of Applied Microbiology 2006, 100:1289-1298.