application of predictive microbiology to control the growth of listeria monocytogenes – dairy...
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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 Quality ManagementFaculty of Food Science
University of Warmia and Mazury in Olsztyn
ISOPOL XVII 2010 , Porto
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
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.
LISTERIA MONOCYTOGENES!!!
Materials and methods
1. Microbiological analyses
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)
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
Pathogen Modeling Program & ComBase Predictor
www.combase.ccpmp.arserrc.gov
Changes in the number of Listeria monocytogenes (log cfu/g) during ripening (13oC/10d) and storage in the temperature range 3-15oC
0 200 400 600 800 10000
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9
3
6
9
12
15
time [h]
log
cfu/
g
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
0 50 100 150 200 2500
1
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9
time [h]
log
cfu/
g
Blue squares– fitted Baranyi model (R2>0.9)
Primary modeling results – fitted Baranyi and Roberts model
0 200 400 600 800 1000 12000123456789
10 Camembert - 3oC
time [h]
log
cfu/g
0 100 200 300 400 500 600 700 8000
1
2
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8
9 Camembert - 6oC
time [h]log
cfu/
g
0 50 100 150 200 250 300 350 400 450 5000
1
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6
7
8
9 Camembert - 9oC
time [h]
log cf
u/g
0 50 100 150 200 250 300 3500
1
2
3
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5
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8
9 Camembert - 12oC
time [h]
log
cfu/
g
0 20 40 60 80 100 120 140 1600
1
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7
8Camembert - 15oC
time [h]
log
cfu/
g
Secondary modeling and validation results – fitted square root model
2 4 6 8 10 12 14 160
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
temperature (oC)
µ [h
-1]
sqrt_mu=b*(temp-tmin) sqrt_mu=0.0023*(temp+0.8088)
Accuracy f actor= 1.22Bias factor = 1.04
Proportion of variance explained (R^2) = 0.9376 (93.76%)
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%
0 200 400 600 800 10000
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7
83oC
time [h]
log c
fu/g
0 100 200 300 400 500 600 700 8000
1
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8
96oC
time [h]
log
cfu/g
0 50 100 150 200 250 300 350 400 450 5000
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8
99oC
time [h]
log
cfu/
g
0 50 100 150 200 250 300 3500
1
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8
912oC
time [h]
log cf
u/g
-10 10 30 50 70 90 110 130 1500
1
2
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8
915oC
time [h]
log cf
u/g
*- observed growth• - PMP∆ - CP
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:
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
KMiZJ
Thank you for attention!
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