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Application of predictive microbiology to control the growth of Listeria monocytogenes – dairy products as an example Adriana Lobacz Chair of Dairy and.

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Presentation on theme: "Application of predictive microbiology to control the growth of Listeria monocytogenes – dairy products as an example Adriana Lobacz Chair of Dairy and."— Presentation transcript:

1 Application of predictive microbiology to control the growth of Listeria monocytogenes – dairy products as an example Adriana Lobacz Chair of Dairy and Quality Management Faculty of Food Science University of Warmia and Mazury in Olsztyn ISOPOL XVII 2010, Porto

2 Risk analyses in food Risk assessmnet Hazard identificationHazard characterisationExposure assessmentRisk characterisation Risk management Risk communication PREDICTIVE MICROBIOLOGY response of the microorganisms on the environmental conditions is reproducible on the basis of experiments and observations it is possible to predict the behaviour of microorganisms in food

3 Mathematical modeling Kinetic parameters of microorganisms growth External factors storage conditions Internal factors product characteristic Environment parameters temperature storage atmosphere water activity pH naturaly presented organic acid preservatives interactions between microorganisms etc.

4 LISTERIA MONOCYTOGENES!!!

5 Materials and methods 1.Microbiological analyses CONTAMINATION LEVEL 1000cfu/g (free of Listeria monocytogenes, Fraser) (37 o C/18hrs; LEB) RIPENING (13 o C/10 days) (ALOA, Merck ) (37 o C/18hrs; LEB) STORAGE CONTAMINATION LEVEL 1000cfu/g (3,6,9,12,15 o C) (ALOA, Merck ) (free of Listeria monocytogenes, Fraser)

6 Materials and methods 2. Predictive modeling primary modeling – Baranyi and Roberts model (1994) & secondary modeling – square root model models validation – bias (B f ) and accuracy (A f ) factors comparison with tertiary models – Pathogen Modeling Program and ComBase Predictor

7 Pathogen Modeling Program & ComBase Predictor www.combase.cc pmp.arserrc.gov

8 Changes in the number of Listeria monocytogenes (log cfu/g) during ripening (13 o C/10d) and storage in the temperature range 3-15 o C NO GROWTH OCCURED DURING THE RIPPENING PERIOD (13 o C/13days) Ryser E.T. et al. J Food Prot 1987: No growth during ripening; All L. mono strains initiated growth after 18d of ripening

9 Blue squares– fitted Baranyi model (R 2 >0.9) Primary modeling results – fitted Baranyi and Roberts model

10 Secondary modeling and validation results – fitted square root model sqrt_mu=b*(temp-tmin) sqrt_mu=0.0023*(temp+0.8088) Accuracy f actor= 1.22 Bias factor = 1.04 Proportion of variance explained (R^2) = 0.9376 (93.76%)

11 Comparison with tertiery models – Pathogen Modeling Program (PMP) and ComBase Predictor (CP) inputs: temperature (3, 6, 9, 12, 15 o C), pH 5.1, NaCl 1.7% * - observed growth - PMP - CP

12 Research project:Application of predictive microbiology to increase food safety 2009-2012, Ministry of Science and Higher Education nr N R12 0097 06 Coordinator – UWM (prof. Stefan Ziajka) Warsaw University of Live Sciences University of Life Sciences in Lublin The Cracow Univeristy of Economics Microbiological risk assessment of dairy and meat products Microbiological analysis in order to evaluate behaviour of foodborne pathogens ( L. monocytogenes, S. enteritidis, Y. enterocolitica, C. jejuni, E. coli ) in particular meat and dairy products Mathematical modeling – generation and validation of primary and secondary models describing the growth of pathogens Developing a database which contains predictive models TASKS:

13 Acknowledgements: Stefan Ziajka Sylwia Tarczynska Jaroslaw Kowalik Project Generation of predictive models to describe the environmental growth conditioning of foodborne pathogens Listeria monocytogenes and Yersinia enterocolitica in dairy products, Ministry of Science and Higher Education nr N312 296935 Supported by the EU within the European Social Fund

14 Thank you for attention! eFoodAlert.com


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