Predictive microbiology - DTU Research Database · Predictive microbiology 2 What is it? Prediction...
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Predictive microbiology Tina Beck Hansen
Hazard course, Bureau Veritas, January 31, 2019
• What is it?
• How is it done?
• What is it used for?
• How is it used correctly?
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DTU Food, Technical University of Denmark 31 January 2019
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Predictive microbiology
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What is it?
Prediction of microbial behaviour in food environments
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DTU Food, Technical University of Denmark 31 January 2019
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The idea is not new!
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•1920’s – heat inactivation: D-, z- and F-values
•1930’s – Scott from Australia: … if we know the growth rates of
the meat spoilage organisms, we can predict when meat is spoiled
at different storage temperatures…
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But it accelerated in the 1980’s…
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• Growth, survival and inactivation of microorganisms in foods are reproducible
responses
• A limited number of environmental parameters in foods determine the kinetic
responses of microorganisms
–Temperature
–Water activity / salt-in-water
–pH
–Preservatives
• Mathematical models that quantitatively describe the combined effect of the
environmental parameters can be used to predict growth, survival or
inactivation of microorganisms
Roberts & Jarvis (1983):
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Growth of Brochothrix within 10 days in 1989
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10 C 3.5 C
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Predictive micorbiology
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How is it done?
Using mathematical equations for description of kinetic responses of microorganisms in food
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Procedure for building microbial predictive models
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Primary modelling
Tertiary modelling
Lag Growth rate Nmax
Secondary modelling
Validation N = No · e µt
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Model types
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Model type Response Predictor Examples
Primary model:
A function describing microbial response over time
CFU
Toxin formation
Metabolic compounds
Absorbance
Impedance
Time
Exponential
Logistic
Gompertz
Baranyi & Roberts
Weibull
Secondary models:
An equation using parameter estimates from a primary model as response
Growth rate
Generation time
Lag phase
Max. population
D-value
Growth/no-growth
Environmental parameters
(Time)
Polynomial
Arrhenius
Square root
CPM-models
ANN-models
z-value
Probability models
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Validation
•Does the developed model work for real life situations?
•Comparison of observed and predicted values
•Graphical and/or mathematical
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Graphical validation
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Observed values
Pre
dic
ted v
alu
es
Fail-safe
predictions
Fail-dangerous
predictions
0.0
0.1
0.2
0.3
0.4
0.5
0.0 0.1 0.2 0.3 0.4 0.5
Pre
dic
ted
gro
wth
rat
e (
log
cfu
/h)
Observed growth rate (log cfu/h)
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Mathematical validation
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Secondary models (growth rates, lag times, D-values)
Bias factor (Bf) =
10(∑log(pred/obs) / n)
Accuracy factor (Af) =
10(∑│log(pred/obs)│ / n)
Illustration: Furqan Nazeeri
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Interpretation of Bias factor
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Interpretation of Bf
Bf > 1, faster growth
Bf < 1, slower growth
Good: 0.95-1.11
Acceptable: 0.87-0.95 or 1.11-1.43
Unacceptable: <0.87 or >1.43
0.0
0.1
0.2
0.3
0.4
0.5
0.0 0.1 0.2 0.3 0.4 0.5
Pre
dic
ted
gro
wth
rat
e (
log
cfu
/h)
Observed growth rate (log cfu/h)
Bf = 0.93
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Validation – dynamic temperature profile
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0
5
10
15
20
25
30
0 4 8 12 16 20 24
Tem
per
atu
r (°C
)
Tid i timer
lufttemp.
kødtemp.
Air
Chicken
Time in hours
0
1
2
3
4
5
6
0 4 8 12 16 20 24
L. m
on
ocy
tog
enes
(lo
g cf
u/s
lice)
tid i timer
observeret
forudsigelse
Time in hours
Observed counts
Predicted counts
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Tertiary modelling – available tools
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https://foodrisklabs.bfr.bund.de/microbial-modeling-exchange-wiki/
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Popular freeware tools
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• Pathogen Modeling Program (PMP)
–USA
–http://ars.usda.gov/services/docs.htm?docid=6786
–http://pmp.errc.ars.usda.gov/PMPOnline.aspx
–>40 models (growth, survival and inactivation)
–Available as freeware
• ComBase
–UK & USA: www.combase.cc
–ComBase Predictor: online models for growth and inactivation for mainly pathogens
–ComBase Perfringens Predictor: online model for evaluation of safe cooling of meat
–ComBase Browser: data for growth or inactivation of food associated microorganisms
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More popular freeware tools
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• Food Spoilage and Safety Predictor (FSSP)
–DK: http://fssp.food.dtu.dk
–Time-temperature integration
–Shelf-life, specific spoilage organisms
–Listeria monocytogenes, histamine formation
• DMRIpredict
–DK: http://dmripredict.dk
–Safety models, L. monocytogenes, Clostridium botulinum, ConFerm, Yersinia enterocolitica, Staphtox predictor, F value calculator
–Shelf-life models, pork, beef and chicken cuts, minced pork, minced beef and bacon
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Example and discussion of predictions
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Listeria
monocytogenes
Generation times in hours
when pH=6, salt-in-water=2 % and no additives
ComBase PMP FSSP DMRI
At 5 oC
At 10 oC
At 20 oC
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Example and discussion of predictions
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Listeria
monocytogenes
Generation times in hours
when pH=6, salt-in-water=2 % and no additives
ComBase PMP FSSP DMRI
At 5 oC 18 13 27 29
At 10 oC 6.7 5.4 10 8.9
At 20 oC 1.7 1.3 3.2 -
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Applications
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What is it used for?
For assessment of microbial food safety and stability:
- Predicting the effect of product characteristics and storage
- Supporting HACCP systems
- Easing education and training activities
- Qualifying QMRA models
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How are microbial predictive models used correctly?
The model
• Is the model validated?
–Broth vs food
• Is it validated for the specific purpose?
–Specific strain
–Temperature range
–Dynamic temperature profile
The input values
• Are the most important environmental factors included in the model?
–Temperature, salt, aw, pH, CO2, others
• How are the values determined?
–Measured vs calculated
–Units
• Have measurements been repeated?
• How is variability of measurements included?
• How is initial count determined?
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Experiment – can we predict temperature abuse from the microbiota in pork?
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Microbiota collection
Inoculation Incubation Sampling
Pooling of carcass-swab and faeces samples
4 oC and 7 oC: chill-stored
12 oC and 16 oC: temperature abuse
16S rRNA gene
sequencing
Predictive modelling
Tasja Buschhardt, 2017
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Temperature driven changes in genus compositions
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Tasja Buschhardt, 2017
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Temperature driven changes in community compositions
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PCoA as a conceptual “ temperature index”, BUT variation needs to be taken into account.
Chill-
sto
red
Faeces Carcass-swabs
Tasja Buschhardt, 2017
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Thank you for your attention