Modeling of Bacillus cereus distribution in pasteurized milk at the time of consumption

Authors

  • Pavel Ačai Slovak University of Technology, Faculty of Chemical and Food Technology, Institute of Chemical and Environmental Engineering, Radlinského 9, 812 37 Bratislava
  • Ľubomí­r Valí­k Slovak University of Technology, Faculty of Chemical and Food Technology, Department of Nutrition and Food Safety Assessment, Radlinského 9, 812 37 Bratislava
  • Denisa Liptáková Slovak University of Technology, Faculty of Chemical and Food Technology, Institute of Biochemistry, Microbiology and Health Protection, Radlinského 9, 812 37 Bratislava
  • Jana Minarovičová Institute of Food Research, Priemyselná 4, 824 75 Bratislava

DOI:

https://doi.org/10.5219/264

Keywords:

Bacillus cereus, predictive model, exposure assessment, Monte Carlo simulation

Abstract

Modelling of Bacillus cereus distributionusing data from pasteurized milk produced in Slovakia, at the time of consumption was performed in this study. The Modular Process Risk Model (MPRM) methodology was applied to over all the consecutive steps in the food chain. The main factors involved in the risk of being exposed to unacceptable levels of B. cereus (model output) were the initial density of B. cereus after milk pasteurization, storage temperatures and times (model input). Monte Carlo simulations were used for probability calculation of B. cereus density. By applying the sensitivity analysis influence of the input factors and their threshold values on the final count of B. cereus were determined. The results of the general case exposure assessment indicated that almost 14 % of Tetra Brik cartons can contain > 104 cfu/ml of B. cereus at the temperature distribution taken into account and time of pasteurized milk consumption.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

References

Anonymous: 1999 U. S. Food Temperature Evaluation Design and Summary Pages. Audits International/FDA, p.13.

Baranyi, J., Roberts, T.A. 1994: A dynamic approach to predicting bacterial growth in food. Int. J. Food Microbiol. vol. 23. p. 277-294, https://doi.org/10.1016/0168-1605(94)90157-0 DOI: https://doi.org/10.1016/0168-1605(94)90157-0

Blackburn, C. W., McClure, P. J. 2009. Foodborne pathogens: Hazards, risk analysis and control, 2 nd. Edition, CRC Press. p. 844-888. ISBN: 143980768X DOI: https://doi.org/10.1533/9781845696337.2.844

Davey, K. R. 1989. A predictive model for combined temperature and water activity on microbial growth during growth phase. J. Appl. Bacteriol. vol. 67, p. 483-488. https://doi.org/10.1111/j.1365-2672.1989.tb02519.x PMid:2592289 DOI: https://doi.org/10.1111/j.1365-2672.1989.tb02519.x

Fazil, A. M. 2005. A primer on risk assessment modelling: focus on seefood products. FAO Fisheries Technical Paper, 462:56.

Granum, P. E., Thomas, J. M., Alouf, J. E. 1995. A survey of bacterial toxins involved in food poisoning: a suggestion for bacterial food poisoning toxin nomenclature. Int. J. Food Microbiol., vol. 28, no. 2, p. 129-144. https://doi.org/10.1016/0168-1605(95)00052-6 DOI: https://doi.org/10.1016/0168-1605(95)00052-6

ILSI (International Life Science Institute) 2012. Tools for microbiological risk assessment. ILSI Europe a.i.s.b.l. ISBN 9789078637349. Retrieved from the web: http://orbit.dtu.dk/files/43551223/MRA%20Tools.pdf

Lampel, K. A., Al-Khaldi, S., Cahill, S. M., 2012. Bad Bug Book. Foodborne Pathogenic Microorganisms and Naturral Toxins Handbook, 2 nd. Edition,. Silver Spring: Food and Drug Administration (FDA). Retrieved from the web: http://www.fda.gov/downloads/Food/FoodSafety/ FoodborneIllness/FoodborneIllnessFoodbornePathogensNaturalToxins/BadBugBook/UCM297627.pdf

Lindquist, R., Sylvén, S., Vägsholm, I. 2002. Quantitative microbial risk assessment exemplified by Staphylococcus aureus in unripened cheese made from raw milk. Int. J. Food Microbiol., vol. 78, no. 1-2, p. 155-170. https://doi.org/10.1016/S0168-1605(02)00237-4 DOI: https://doi.org/10.1016/S0168-1605(02)00237-4

Nauta, M. J. 2000 Separation of uncertainty and variability in quantitative microbial risk assessment models. Int. J. Food Microbiol., vol. 57, no. 1-2, p. 9-18. https://doi.org/ 10.1016/S0168-1605(00)00225-7 DOI: https://doi.org/10.1016/S0168-1605(00)00225-7

Nauta, M. J., Litman, S., Barker, G. S., Carlin, F. 2003. A retail and consumer phase model for exposure assessment of Bacillus cereus. Int. J. Food Microbiol., vol. 83, no. 2, p. 205-218. https://doi.org/10.1016/S0168-1605(02)00374-4 DOI: https://doi.org/10.1016/S0168-1605(02)00374-4

Nauta, M. J. 2005. Microbial risk assessment models for portioning and mixing during food handling. Int. J. Food Microbiol., vol. 100, no. 1-3, p. 311-322. https://doi.org/10.1016/j.ijfoodmicro.2004.10.027 PMid:15854714 DOI: https://doi.org/10.1016/j.ijfoodmicro.2004.10.027

Notermans, S., Dufrenne, J., Teunis, P., Beumer, R., te Gipfel, M., Peeters Weem, P. 1997. A risk assessment study of Bacillus cereus present in pasteurized milk. Food Microbiol., vol. 14, no. 2, p. 143-151. DOI: https://doi.org/10.1006/fmic.1996.0076

Notermans, S., Nauta, M. J., Jansen, J., Jouve, J. L., ,Mead, G. C. 1998. A risk assessment approach to evaluating food safety based on product surveillance. Food Control, vol. 9, no. 4, p. 217-223. https://doi.org/10.1016/S0956-7135(97)00086-8 DOI: https://doi.org/10.1016/S0956-7135(97)00086-8

Mataragas, M., Zwietering, M. H., Skandamis, P. N., Drosinos, E. H. 2010. Quantitative microbial risk assessment as a tool to obtain useful information for risk managers - Specific application to Listeria monocytogenes and ready-to-eat meat products. Int. J. Food Microbiol. vol. 141, p. S170-S179. https://doi.org/10.1016/j.ijfoodmicro.2010.01.005 PMid:20116877 DOI: https://doi.org/10.1016/j.ijfoodmicro.2010.01.005

Pokrievka, M. 2001. Distribution of temperatures in domestic refrigerators. Bratislava: FCHPT STU.

Ratkowsky, D. A., Olley, J., McMeekin, T. A. and Ball A. 1982. Relationship between temperature and growth rate of bacterial cultures. J. Bacteriol., vol. 149, p. 1-5. PMid:7054139 DOI: https://doi.org/10.1128/jb.149.1.1-5.1982

Valík, Ľ., Görner, F. and Lauková, D. 2003. Growth dynamics of Bacillus cereus and shelf-life of pasteurized milk. Czech J. Food Sci., vol. 21, no. 6, p. 195-202. DOI: https://doi.org/10.17221/3498-CJFS

Vose, D. 2008. Risk Analysis: A Quantitative Guide. 3rd ed. John Wiley & Sons, Ltd., ISBN 978-0-470-512845-5

Risk analysis modeling for Excel. Retrieved vrom the web: http://www.vosesoftware.com/

Zwietering, M. H, Notermans, S. and de Wit, J. 1996. The application of predictive microbiology to estimate the number of Bacillus cereus in pasteurized milk at the point of consumption. Int. J. Food Microbiol. vol. 30, no. 2, p. 55-70. https://doi.org/10.1016/0168-1605(96)00991-9 DOI: https://doi.org/10.1016/0168-1605(96)00991-9

Downloads

Published

2013-07-09

How to Cite

Ačai, P. ., Valí­k, Ľubomí­r ., Liptáková, D. ., & Minarovičová, J. . (2013). Modeling of Bacillus cereus distribution in pasteurized milk at the time of consumption. Potravinarstvo Slovak Journal of Food Sciences, 7(1), 63–66. https://doi.org/10.5219/264

Most read articles by the same author(s)