Development of a GrowthNo Growth model based on growth data of 10 different L. monocytogenes strains
1. Development of a Growth/No Growth model using growth data of 10
different Listeria monocytogenes strains
Pantelis J. Stathopoulos
Food Technologist
MSc
2011
5. L. monocytogenes
• Gram+ bacterium,
• Potentially pathogenic,
• Resistant to severe values of pH/ aw
• Widespread in the environment.
• Temperature (Walker et al., 1990),
• pH (Farber et al., 1989; Buchanan et al., 1993)
• Water activity (Farber et al., 1992; Nolan et al., 1992)
Generally
Environmental Factors
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6. • pH: 4,0 – 8,0
• aw: 0,90 – 0,99
• Temperature: -2 – 45 oC
• Great variability of growth limits (Augustin and Carlier, 2000):
– Acid used for pH
– Humectants used for water activity
– Substrate used
– Experimental conditions
L. monocytogenes
1
Growth limits
8. Statistical Models
Primary Models
• Mathematical description of growth kinetics (e.g. μmax, generation time)
Secondary Models
• Mathematical description of the effect of several environmental factors (e.g.
temperature, pH, aw) on the growth limits of a microorganism
Tertiary Models
• Combination of several secondary models for the production of a software
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9. Growth/ No Growth Models
Importance of Growth/No Growth models
• Study of the combined environmental factors that prevent microbial
growth.
• Ability to retrieve data about the growth boundary of microorganisms
on synthetic substrates, in order to reduce significantly the number of
challenge tests necessary to determine the limits of growth in real food
1
10. Tienungoon et al., 2000.
Growth/ No Growth Models
Growth
No - Growth
P < 10%
P < 50%
P < 90%
Growth
No - Growth
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11. Present State
G/NG models used for:
• Effect of antimicrobial substances on growth
(e.g. nisin, Boziaris and Nychas, 2006)
• Effect of the initial inoculum level on growth
(Vermeulen et al., 2009)
• Effect of novel methods of food processes on growth
(e.g. HHP, Bover-Cid et al. 2010, Pulse Light Hierro et al., 2011)
• Strain Variability
(Valero et al., 2010; Lianou and Koutsoumanis, 2011)
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12. Present State
Microbial Strains differ as
• For their origin of isolation (e.g. food, environment, clinical)
• For their serotype
• For their genotype
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13. Present State
Strain Variability and Statistical Modeling
• Escherichia coli
Modeling of five different strains of E. coli
(Valero et al., 2010)
• Salmonella spp.
A stochastic approach for integrating strain variability in modeling growth of
Salmonella enterica
(Lianou and Koutsoumanis, 2011).
• Listeria monocytogenes
Two different strains of L. monocytogenes, produced two different statistical
models (Tienungoon et al., 2000).
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14. Present State
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Screening of Strains
• Screening of strains in order to choose the most resistant one
(Vermeulen et al., 2007).
• From a set of strains, the most resistant one is chosen
• In order to define the most resistant strain as for an environmental factor (e.g. pH),
all the other factors (e.g. temperature, water activity) remain constant at their
optimum value
15. Present State
1
Screening of strains
pH
6.8
6.4
6.0
5.6
5.2
4.8
4.4
4.0
pH
6.8
6.4
6.0
5.6
5.2
4.8
4.4
4.0
aw
Simultaneously
Effect of pH/ aw/ Τ/ il
Successive
Effects of pH/ aw
aw
17. Aim of Study
2
General Perspective
• Data from different strains produce different statistical models?
• The uncertainty of the predictions of statistical models that are developed
using data of one “representative” strain can be lifted with the
development of a composite model using growth data of more than one
strain?
18. Aim of Study
2
In this study:
• For 10 different L. monocytogenes strains developed
10 different Growth/No Growth models
• Comparison of individual Growth/No Growth models
• Development of a composite Growth/No Growth model
3
SM-1 SM-2 SM-3 SM-4 SM-5 SM-6 SM-7 SM-8 SM-9 SM-10
Composite G/NG model
Individual G/NG models
1098765421
22. aw / NaCl, NaCl - KCl
3
m (ΝaCl – KCl)/ (mol kg-1)
aw
Calculation of the appropriate quantities of NaCl and NaCl-KCl
KCl
NaCl
NaCl – KCl
0.91
1.36
1.36 mol (NaCl – KCl)
60% NaCl: 0.6 x 1.36 x MΜ = …. NaCl
40% KCl: 0.4 x 1.36 x MΜ = …. KCl
aw
24. Statistical Analysis
Logistic Regression
• Statistical Treatment applied when the results we study are
binomial (0, 1)
– e.g. Growth/ No Growth,
Toxin/ No Toxin,
• Expressed by the logarithm of the probability of occurrence or non-occurrence or
logit P
logit P = Ln
P
1-P
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26. Logit P =
Ln (P/1-P) = b0 +
b1 x pH +
b2 x aw +
b3 x il +
b4 x pH x aw +
b5 x il x pH +
b6 x il x aw
Methodology
Logistic Regression
Parameter il
Interaction pH x aw
Interaction il x pH
Interaction il x aw
Parameter aw
Parameter pH
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27. Methodology
• Logistic Regression process through Minitab Software
• Level if Significance α < 0.05
• Rejection of parameters (pH2, aw
2, il2)
P – value > 0.800
Logistic Regression
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35. Results and Discussion
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NOT FEASIBLE TO CHOOSE ONE AS THE MOST RESISTANT
Factors affecting the Growth Limits
Different Inoculum Levels
Different L. monocytogenes strains
Temperature
Ρυθμιστής aw
Probably
Different
Statistical
Models
EVERY STRAIN HAS DIFFERENT BEHAVIOUR AT DIFFERENT EXPERIMENTAL CONDITIONS
36. Growth profiles and logistic regression
0.87
0.89
0.91
0.93
0.95
0.97
0.99
6.8
6.4
6.0
5.6
5.2
aw
0.870.87
0.89
0.91
0.93
0.95
0.97
0.99
0.870.87
0.89
0.91
0.93
0.95
0.97
0.99
1 3 2
aw aw
Logit P = -242,61 + 6,66 pH + 207,43 aw
Logit P = 1037,63 - 213,99 pH - 1134,44 aw + 0,83 il + 230,81 pH aw
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37. 1. Growth profiles completed after 4 replications
2. Logistic Regression treats every replication as an individual situation
Growth profiles and logistic regression
Strains with the same Growth profile produce
different logistic regression equations
CAUSE
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38. Results and Discussion
4
Most Resistant Strains
2 log cfu/mL
3 log cfu/mL
4 log cfu/mL
1 - 10
2 log cfu/mL
3 log cfu/mL
4 log cfu/mL
4oC pH
18oC
NaCl
NaCl
1 - 10
5
1 - 10
aw
1 - 10
6
1 - 10
Comb
1 - 10
5, 6
7
pH
1, 7
1, 7
1, 7
aw
1, 7
1, 7
1, 7
Comb
1, 7
1, 7
• Considered Solution: Use of a mixed
microbial culture
– Not expected results, the model of the
mixed culture was almost the same with the
individual model (Vermeulen et al., 2007)
39. • Same procedure as for the individual models
• Use of growth data from 10 different strains
Composite Growth/No Growth Model
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43. Used Probability 10% (P = 0.1)
Composite vs. Individual Models
Comparison
of the
Composite Models
with the
Individual Models
of the more resistant strains
at each temperature and different water activity types
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45. Composite vs. Individual Models
composite
M 1
M 9
M 9
M 9
M 1
Χ
4 log
2 log
3 log
Χ
Χ
NaCl- KCl NaCl- KCl
composite
M 6
M 8
46. Composite vs. Individual Models
Observations
• At certain environmental conditions, the individual models fail to predict the
growth of Listeria monocytogenes.
• At certain environmental conditions, the composite model has a smaller growth
boundary than the individual models. Despite that, it safely predicts every
condition where Listeria monocytogenes did grow.
– This means that in some cases the composite model gives less conservative
observations
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48. Conclusions
• Strain Variability as for their Growth Boundary
• Change of a single environmental condition (π.χ. pH) leads to a change of the
Growth profile
• Even strains with the same Growth profile can produce different statistical models
• Development of a Growth/No Growth model using growth data of a single strain can
lead to false estimations
• Development of a composite Growth/No Growth model using growth data of as
many as possible strains under several environmental conditions and inoculum levels
could lead to safer conclusions
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