Probabilistic Model for Listeria monocytogenes Growth during Distribution, Retail Storage, and
Development of a GrowthNo Growth model based on growth data of 10 different L. monocytogenes strains
48
Development of a Growth/No Growth model using growth data of 10 different Listeria monocytogenes strains Pantelis J. Stathopoulos Food Technologist MSc 2011
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Transcript of Development of a GrowthNo Growth model based on growth data of 10 different L. monocytogenes strains
Development of a Growth/No Growth model using growth data of 10 different Listeria monocytogenes strains
Pantelis J. Stathopoulos Food Technologist
MSc
2011
Presentation Structure
Introduction
The aim of the study
Methodology
Results and Discussion
Conclusions
1
2
3
4
5
INTRODUCTION 1
General
Listeria monocytogenes
1
Environmental Factors
Mathematical Modeling
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
1
• 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
Statistical Growth Models
Predictive Microbiology (McMeekin et al., 1993)
L. monocytogenes
PATHOGENICITY WIDE DISTRIBUTION RESISTANCE
1
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
1
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
Tienungoon et al., 2000.
Growth/ No Growth Models
Growth
No - Growth
P < 10% P < 50% P < 90%
Growth
No - Growth
1
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)
1
Present State
Microbial Strains differ as
• For their origin of isolation (e.g. food, environment, clinical)
• For their serotype
• For their genotype
1
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).
1
Present State 1
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
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
AIM OF STUDY 2
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?
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
10 9 8 7 6 5 4 2 1
METHODOLOGY
3
Methodology 3
10 L. monocytogenes strains
1
2
3
4
Clinical strains C
Food strains F
5
6
7
8
«Reference» strains
S-1
S-4
S-7
S-8
S-3
S-5
S-6
S-9
9 10
S-1 S-10
Model Parameters 3
5760 combinations
aw
NaCl
NaCl – KCl
pH
CH3COOH
T
Inoculum Level
102 cfu/mL
103 cfu/mL
104 cfu/mL
Χ 10 strains
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
Methodology 3
SUBSTRATES
Inoculum
96 vials (ΒΗΙ broth)
102 cfu/mL
103 cfu/mL
104 cfu/mL
TIME
60 days
4οC
18οC
102 cfu/mL
103 cfu/mL
104 cfu/mL
Brain Heart Infusion
pH
aw
102 cfu/mL
103 cfu/mL
104 cfu/mL
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
3
Methodology
Logistic Regression
DATA (Environmental Factors)
RESPONSE (0, 1)
Link Function
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
3
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
3
Methodology
• Logistic Regression process through Minitab Software
• Level if Significance α < 0.05
• Rejection of parameters (pH2, aw2, il2)
P – value > 0.800
Logistic Regression
3
RESULTS AND DISCUSSION
4
Results and Discussion
Effect of the initial inoculum level for all experimental conditions (aw, pH, T)
for all strains
4
4
pH
0.8
7
0.8
9
0.9
1
0.9
3
0.9
5
0.9
7
0.9
9
6.8
6.4
6.0
5.6
5.2
aw
2 log
0.8
7
0.8
9
0.9
1
0.9
3
0.9
5
0.9
7
0.9
9
6.8
6.4
6.0
5.6
5.2
0.8
7
0.8
9
0.9
1
0.9
3
0.9
5
0.9
7
0.9
9
6.8
6.4
6.0
5.6
5.2
5 6
5
5
Growth Limits (4οC / NaCl)
aw
pH
3 log
4 log
1 -10 1 -10
5
6
1-10
5
6
4
pH
0.8
7
0.8
9
0.9
1
0.9
3
0.9
5
0.9
7
0.9
9
6.8
6.4
6.0
5.6
5.2
aw
0.8
7
0.8
9
0.9
1
0.9
3
0.9
5
0.9
7
0.9
9
6.8
6.4
6.0
5.6
5.2
0.8
7
0.8
9
0.9
1
0.9
3
0.9
5
0.9
7
0.9
9
6.8
6.4
6.0
5.6
5.2
6 8
6 8
Growth Limits (4οC / NaCl - KCl)
pH
2 log 3 log
4 log
1 -10 1-10
6 8
1-10
5 6 8
1-10
4 6 8 10
6 8
6 8
6 8
6 8
6 8
Growth Limits (18οC / NaCl)
4
pH
0.8
7
0.8
9
0.9
1
0.9
3
0.9
5
0.9
7
0.9
9
6.8
6.4
6.0
5.6
5.2
aw
0.8
7
0.8
9
0.9
1
0.9
3
0.9
5
0.9
7
0.9
9
6.8
6.4
6.0
5.6
5.2
0.8
7
0.8
9
0.9
1
0.9
3
0.9
5
0.9
7
0.9
9
6.8
6.4
6.0
5.6
5.2
1 5 6
1 3 8
1 7
1 2 6 9 10
1 5 7
1 6 7 9
1 3 7 8
1 9
1 4 5 6 7 8
1-10
1 5 7
1 6 7 9
1 3 7 8 9
1 9 1 -10
1 3 4 5 6 7 8 10
aw
1 7
1
1 7
1
1 7
1
1 7
1
1 7
1 7
1 7
1 7
1 7
4 5 7 8
1
2 log 3 log
4 log
7
4
pH
0.8
7
0.8
9
0.9
1
0.9
3
0.9
5
0.9
7
0.9
9
6.8
6.4
6.0
5.6
5.2
aw
0.8
7
0.8
9
0.9
1
0.9
3
0.9
5
0.9
7
0.9
9
6.8
6.4
6.0
5.6
5.2
0.8
7
0.8
9
0.9
1
0.9
3
0.9
5
0.9
7
0.9
9
6.8
6.4
6.0
5.6
5.2
1
1 3-10
1 3-10
1 3-10
3-10
1
1-10
1-10
2-10
1-10
1 7 9
1-10
1 9
1-10
9 2-10
1 9
1 9
9
1 9
1 9
1 9
1 9
1
1 9
1 9
1 9
1
aw
Growth Limits (18οC/ NaCl – KCl)
1 9
9 9
2 log 3 log
4 log
pH
p
H
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
2 log cfu/mL
3 log cfu/mL
4 log cfu/mL
2 log cfu/mL
3 log cfu/mL
4 log cfu/mL
4oC
18oC
NaCl
NaCl
NaCl/ KCl
NaCl/ KCl
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
1 - 10
pH
6, 8
6, 8
1 - 10
aw
1 - 10
6, 8
1 - 10
Comb
1 - 10
6, 8
1, 9
pH
1, 9
1, 9
1
aw
1
1, 9
1, 9
Comb
1, 9
9
T Π Ν
Ν Τ
Results and Discussion 4
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
Growth profiles and logistic regression
0.8
7
0.8
9
0.9
1
0.9
3
0.9
5
0.9
7
0.9
9
6.8
6.4
6.0
5.6
5.2
aw
0.8
7
0.8
7
0.8
9
0.9
1
0.9
3
0.9
5
0.9
7
0.9
9
0.8
7
0.8
7
0.8
9
0.9
1
0.9
3
0.9
5
0.9
7
0.9
9
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
4
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
4
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)
• Same procedure as for the individual models
• Use of growth data from 10 different strains
Composite Growth/No Growth Model 4
0.8
7
0.8
9
0.9
1
0.9
3
0.9
5
0.9
7
0.9
9
6.8
6.4
6.0
5.6
5.2
1 2 n
0.8
7
0.8
9
0.9
1
0.9
3
0.9
5
0.9
7
0.9
9
0.8
7
0.8
9
0.9
1
0.9
3
0.9
5
0.9
7
0.9
9
C
0.8
7
0.8
9
0.9
1
0.9
3
0.9
5
0.9
7
0.9
9
Composite Growth/No Growth Model
Methodology for combining data aw aw aw
pH pH pH
4
G/ NG Model (NaCl)
2 log
3 log
4 log
6
6
9
4oC
4oC
4oC
18oC
18oC
18oC
20
21
21
aw aw
aw aw
G/ NG Model (NaCl - KCl)
4oC
4oC
4oC
18oC
18oC
18oC
6
7
10
18
18
20
2 log
3 log
4 log
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
4
4oC
4oC
4oC
18oC
18oC
18oC
composite
M 1
M 7
Composite vs. Individual Models M 1
M 1
M 5
NaCl Χ
Χ
Χ
composite
M 5
M 6
NaCl
4 log
2 log
3 log
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
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
4
CONCLUSIONS
5
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
5
