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. Hazard identification Hazard characterisation Risk characterisation
Risk analyses in food
Risk assessmnet
Hazard identification
Hazard characterisation
Exposure assessment
Risk 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 Microbiological analyses RIPENING STORAGE
CONTAMINATION LEVEL 1000cfu/g
(free of Listeria monocytogenes, Fraser)
(37oC/18hrs; LEB)
RIPENING
(13oC/10 days)
(ALOA, Merck)
(37oC/18hrs; LEB)
STORAGE
CONTAMINATION LEVEL 1000cfu/g
(3,6,9,12,15oC)
(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 (Bf) and accuracy (Af) factors
comparison with tertiary models – Pathogen Modeling Program and ComBase Predictor

7. Pathogen Modeling Program & ComBase Predictor
pmp.arserrc.gov

8. NO GROWTH OCCURED DURING THE RIPPENING PERIOD (13oC/13days)
Changes in the number of Listeria monocytogenes (log cfu/g) during ripening (13oC/10d) and storage in the temperature range 3-15oC
NO GROWTH OCCURED DURING THE RIPPENING PERIOD (13oC/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. Primary modeling results – fitted Baranyi and Roberts model
Blue squares– fitted Baranyi model (R2>0.9)

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

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

12. Microbiological risk assessment of dairy and meat products
Research project:”Application of predictive microbiology to increase food safety” , Ministry of Science and Higher Education nr N R
University of Life Sciences in Lublin
Coordinator – UWM (prof. Stefan Ziajka)
Warsaw University of Live Sciences
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 N
Supported by the EU within the European Social Fund

14. Thank you for attention!
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