Transcript Establishing shelf life: Use of Predictive modelling

Establishing shelf life: Use of
Predictive modelling
Linda Everis
[email protected]
Tel 01386 842063
What is Predictive microbiology?
• Use of mathematical equations to describe
microbiological behaviour e.g.
– how bacterial populations change with time
– how this change is affected by intrinsic and extrinsic
parameters such as pH and temperature
• This information is used to predict likely
responses in new previously untested
situations
Types of predictions available
• Kinetic growth models - lag time, growth rate,
time to reach target level of numbers
• Kinetic death models - time to reach a target
decrease in numbers
• Growth/no-growth or growth boundary
models - likelihood of growth occurring within
a defined time
• Time to growth models - likely time to reach
visible growth
Which variables can be changed
in models?
• Models are usually limited to 3 or 4
variables e.g.
– Temperature of storage
– pH
– Water activity (salt/sugar etc)
– Preservatives/MAP/nitrite etc.
How are models produced?
Choose organisms

Collect growth data from laboratory
experiments under a range of conditions

Apply appropriate mathematical equations to
produce model

Verify the predicted values against data
produced in real foods
Kinetic growth models
Log (Count)
10
•
5
0
0
1
2
3
4 5 6 7
Time (Days)
8
9
10
Fitted growth curve
Log (Count)
10
5
0
0
1
2
3
4 5 6 7
Time (Days)
8
9
10
Curves from different conditions
10
10
10
8
8
8
6
6
6
4
4
4
2
2
2
0
0
0
5
10
0
0
5
10
10
10
10
8
8
8
6
6
6
4
4
4
2
2
2
0
5
10
5
10
0
5
10
0
0
0
0
0
5
10
10
10
10
8
8
8
6
6
6
4
4
4
2
2
2
0
0
0
0
5
10
0
5
10
0
5
10
Curves from different conditions
10
10
10
8
8
8
6
6
6
4
4
4
2
2
2
0
0
0
5
10
0
0
5
10
10
10
10
8
8
8
6
6
6
4
4
4
2
2
2
0
5
10
5
10
0
5
10
0
0
0
0
0
5
10
10
10
10
8
8
8
6
6
6
4
4
4
2
2
2
0
0
0
0
5
10
0
5
10
0
5
10
Primary model
12
12
10°C
15°C
y max=8.64
8
8
y max=6.37
µ=0.0158
4
µ=0.103
4
lambda=56.1
0
lambda=23.3
150
300
450
600
12
0
20
40
60
80
12
20°C
25°C
y max=9.02
8
y max=8.70
8
µ=0.218
µ=0.420
4
4
lambda=6.01
0
lambda=4.21
15
30
45
60
0
12
24
36
48
12
30°C
y max=9.66

8
y  y0  ln 1  e-   e  time  
µ=0.785
4
6

  ymax  y0 ln 1  e
lambda=4.53
0
Primary model
12
18
24


10ln 1 e -    e   time 
 y
max
 y 0 10

 e 10 10
Example of a modelling interface
Example of a modelling interface
Modeling systems
• Pathogen models
– Combase predictor (CP)
http://browser.combase.cc/ComBase_Predictor.aspx?mo
del=1
– USDA Pathogen Modelling Programme (PMP)
http://pmp.errc.ars.usda.gov/PMPOnline.aspx?ModelID=
2&Aerobic=True
– systems combined in a joint initiative(COMBASE) to
make microbial response data freely available
• Spoilage models
– FORECAST
– Specific food types
Seafood Spoilage and Safety Predictor
Pathogen growth models
Aeromonas hydrophila (anaerobic, aerobic)
Aeromonas hydrophila anaerobic
Bacillus cereus (anaerobic, aerobic)
Clostridium botulinum (non-proteolytic)
Clostridium botulinum (proteolytic)
Clostridium perfringens
Escherichia coli O157:H7 (anaerobic, aerobic)
Listeria monocytogenes (NaCl/aw) (anaerobic, aerobic)
Salmonella (aerobic)
Staphylococcus aureus (anaerobic, aerobic)
Yersinia enterocolitica (aerobic)
Shigella flexneri (anaerobic, aerobic)
Thermal death /inactivation models
Clostridium botulinum (non-proteolytic)
Escherichia coli O157:H7
Listeria monocytogenes
Saccharomyces cerevisiae
Salmonella (NaCl)
Salmonella (glucose)
Yersinia enterocolitica
Campden BRI FORECAST
SYSTEM
• Exclusively for Spoilage organisms
• Describes
– kinetic growth
– time to turbidity
• for
– individual spoilage organisms
– groups of organisms
– product specific spoilage flora
Models in FORECAST
Temperature (C)
NaCl
(% aq)
Equivalent Aw
pH
Other Conditions
Pseudomonas
0 - 15
0.0 - 4.0
1.00 - 0.977
5.5 - 7.0
Fluctuating temperature, pH, salt
Bacillus spp.
5 - 25
0.5 - 10
0.997 - 0.935
4.0 - 7.0
Fluctuating temperature, pH, salt
Enterobacteriaceae
0 - 27
0.5 - 10
0.997 - 0.935
4.0 - 7.0
Fluctuating temperature, pH, salt
Yeasts
(chilled foods)
0 - 22
0.5 - 10
0.997 - 0.935
2.6 - 6.0
Fluctuating temperature, pH, salt
2.0 - 7.0
0 - 60% Sucrose (w/v)
0 - 20% Ethanol (v/v)
Potassium sorbate
0 - 1000(ppm)
Model
Yeasts
(fruit/drinks)
(time to growth)
0 - 22
-
Lactic acid bacteria
2 - 30
0.5 - 10
0.997 - 0.935
3.0 - 6.0
Fluctuating temperature
Meat spoilage
2 - 22
0-6
1.00 - 0.964
4.6 - 7.0
0 - 240 KNO2 (ppm)
Fluctuating temperature, pH, salt
Fish spoilage
2 - 22
0-6
1.00 - 0.964
4.5 - 8.0
Fluctuating temperature, pH, salt
Fresh produce TVC
2 - 25
-
-
-
2 - 25
-
-
-
2 - 25
-
-
-
2 - 25
-
-
-
52 to 64
0-8
1.00 - 0.95
4.0 - 7.0
Bacillus (time to growth)
8 - 45
0.5 - 10
0.997 - 0.935
4.0 - 7.0
Bacillus (time to growth)
5 - 45
1.33-17.5
0.845-0.988
3.48-5.03
Fresh produce
Enterobacteriaceae
Fresh produce lactic acid
bacteria
Fresh produce Pseudomonas
Enterobacteriaceae death
model
Predicts D value
Model development:
Fresh Produce
• Washed/shredded iceberg lettuce
• Gas mixes:
1= 5% O2: 5% CO2: 90% N2
2= 10% O2: 5% CO2: 85% N2
3= 5% O2: 95% N2
• Storage temperatures : 2,5,8,10,15,25oC
• TVC, Enterobacteriaceae, Lactic acid
bacteria and Pseudomonas enumerated
• Data suggests MAP has no effect on
levels
Entero (mean)
0
100
200
2
5
8
10
12
15
Variable
gas1
gas2
gas3
8
6
log cfu/g
4
8
6
4
25
0
8
6
4
0
100
200
time
Panel variable: temp
100
200
Log cfu/g
Prediction of fresh produce
shelf-life at 8°C
10
8
6
4
2
0
TVC
Pseuds
0
100 200 300 400 500 600
Time (hour)
Entero
Application of predictive
models to the food industry
• Combase Predictor
• Pathogen Modelling Program
• Campden BRI Forecast
• Campden BRI Acid club models
What can be predicted?
•
•
•
•
•
Lag times
Growth rate
Time to reach a target level e.g. 105
Numbers reached in X hours e.g. 96
Likelihood of spoilage i.e. stability, risk,
shelf life, best formulations and
processes
Industrial applications of
modeling systems
• New product development
– define final product formulation
– evaluate recipe changes
– rapid assessment of shelf-life
– affect of storage temperature
– help focus resources for microbial tests
Industrial applications of
modeling systems
• Trouble shooting to assess process
deviations
– decrease in process temperature
– increased pH value
• Setting microbiological specifications
– determine likely levels of bacteria present at end of
shelf-life
– set GMP levels to ensure specifications are met
EVALUATION OF DIFFERENT
SHELF LIFE PROTOCOLS
Aim
Use Predictive Microbiological Models in order to determine the
likely differences between the six shelf life testing protocols.
Conditions
Time 10 day period
pH
6.5
Salt
1.0%
Organisms
Enterobacteriaceae
Pseudomonas
Meat spoilage organisms
End-of-life levels
106 cfu/g
The above conditions have been used for illustrative purposes only and are not intended to
represent actual food products or associated microorganisms
PROTOCOLS SUPPLIED
DAY
0
1
2
3
4
5
6
7
8
9
10
A
4*
4
4
4
4
4
4
4
4
10
10
B
C
D
E
4
4
4
4
2 4h@22
4 2h@22
4
8
4
6
4
8 2h@22
6
6
8
8
6
6
8
8
6
6
8
8
6
6
8
8
6
6
8
8
6
6
8
8
6
6
8
8
6
F
4h@22
8
8
8
8
8
8
8
8
8
Shelf-life in days
Protocol
Enterobacteriaceae
Pseudomonas
Meat spoilage
A
8
4.2
7
B
6
4
5
C
3.2
3
3
D
4
3
4
E
5
3
4
F
1.8
2
3
Product development
Which will spoil fastest?

12C
pH5
?
12C&pH5
or
or
6C
pH7
or 6C&pH7

?
Models in product formulation
2 different conditions - which will spoil fastest ?
pH
5
Temp
ºC
12
7
6
6
Lag
Time to 10
time (h) hours
13
42
44
130
from Forecast
Predicted growth of meat spoilage
organisms as affected by water activity
(other conditions held constant at 8ºC, 100ppm nitrite and pH 6.0)
9
8
7
Log(Count)
6
5
Aw 0.99
Aw 0.98
Aw 0.971
Aw 0.96
4
3
2
1
0
0
50
100
150
200
250
Time/Hrs
300
350
400
450
500
Pathogen predictions
Product
Organism
Initial
level
pH
Salt
(%) or
aw
Temperature
(°C)
Time (h) for
0.5 log cfu/g
increase
Corrected
C.bot incl
safety factor
(1.5)
Time (h)
to 100
cfu/g
Cooked beef
C.botulinum
10
cfu/g
6.16
0.978
5°C for 96h 8°C
for 264h (15d
total)
503 (20d)
335 (13d)
NA
115 (4d)
NA
139 (5d)
10
cfu/g
6.10
296 (12d)
197 (8d)
NA
109 (4d)
NA
127 (5d)
121(5d)
NA
145(6d)
503(20d)
335(13d)
NA
121(5d)
NA
145(6d)
Listeria
Cooked beef
C.botulinum
Listeria
0.985
Listeria
Cooked beef
C.botulinum
Listeria
10
cfu/g
5.92
0.98
Use of predictive models to demonstrate the
effect of frankfurter aw on microbial growth
Data assumes a constant pH of 6.0, and a constant temperature of 8ºC
Organisms
Aw 0.95
Aw 0.99
Lag time (hr)
Time to reach Lag time (hr)
106/g a (hr)
Time to reach
106/g a (hr)
Enterobacteriaceae
1050
Not reached
30
92
Pseudomonas
55
132b
33
77
Meat spoilage
organisms
84
328c
16
104
Lactic acid bacteria
1400
Not reached
44
107
Salmonella
100
710d
42
340
L.monocytogenes
100
500
30
180
B.cereus
300
1000
70
200
Cl. botulinum
600
1400e
130
310
E.coli (10ºC) f
130
1000g
25
200
S. aureus
100
700
50
390
(psychrotrophic)
Pathogen predictions
• C.botulinum
– Time 0.5 log increase (apply safety factor
1.5)
• Listeria
– Time 0.5 log increase
– Time to 100 cfu/g
How reliable are models
• Need to ensure the model shows a good fit to
the original data
• Need to ensure that the model was validated
in foods
• Need to back up model predictions by
laboratory studies in your own foods
• but models can produce reliable estimations
of microbial responses to environmental
conditions
Advantages of using models
• Rapid assessment of the likely shelf-life of
new product formulations
• Trouble shooting to predict effects of process
deviations e.g. increase in temperature
• Focus resources for microbial tests
• models will not replace microbial testing but
will indicate which formulations should be
tested in the laboratory
Limitations of using models
• Predictions are ONLY predictions and need to be
backed up with challenge tests/shelf life studies
• Models only usually take into account 3 or 4 factors
• Input parameters tend to be aw/salt, pH, temperature
• Models do not account for all factors that maybe
present in a food product (e.g. natural antimicrobials
or organic acids)
Limitations of using models
• Most models are food validated but
they don’t take into account food matrix
• Models may tend to be fail safe i.e.
predict growth when growth wouldn’t be
observed in a real foodstuff