validating thermal process lethality in low moisture food
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Validating Thermal Process
Lethality in Low Moisture Food
Approaches to Modeling
Lisa A. Lucore, Ph.D.
The Kellogg Company, Battle Creek, MI 49017
GMA Science Forum - April 4, 2012
1
Topics in Brief
• Why is lethality in Low Aw foods needed?
• Variables that influence lethality in Low Aw
foods?
• Creating a robust predictive model
– Framework
– Significant variables
– Choices for underlying calculation process
2
Why Lethality Models for Low Aw Food?
• Count reduction using surrogate or target
organism is simpler in principle, but may be
challenging to execute.
– Changes to process parameters may require new test.
– May be cost prohibitive for many.
• Development of model to calculate lethality within
process allows changes to the process to be
analyzed and predicted.
– Robust if designed effectively and validated.
– Food forms of comparable composition may be able to
utilize similar models for specified pathogens.
3
Limited Information Available
• Few published D & z values relevant to low Aw foods:
– May have test parameters different from desired production
environment, different test substrate, etc.
• Studies underway for some commodities:
• Individual companies work on data collection but may not
have clear avenue to share findings.
TDT tubes in water Animal feed Liu, et. al, 1969
TDT tubes in oil Chocolate syrup Sumner, et. al, 1991
Mixer cup in oil bath Milk chocolate Goepfert and Biggie, 1968
Swept surface kettle Milk chocolate Barille & Cone, 1970.
Open dishes Flour Archer, et. al, 1998
Almonds, Wheat Kernels, Dates, Powders & Pastes Marks, et. al, 2011
Peanut Paste & Oil GMA, 2011
4
Model Structure: Overview • Format:
– Collect TDT (D & z) values in sealed system.
• Prevents loss of moisture in test; which may skew results of TDT
– Use general method calculation such as American Meat Institute
Model (AMI, 2010), but adjust D&z values based on Aw changes
in thermal process.
• This transitions the model from calculating F-value to Log reduction
• Primary Variables in Model:
– Internal Food Temperature, Dwell Time, Aw
• Model accuracy requires precise measurement of primary
variables, understanding of heat penetration of the food
and temperature mapping of the thermal system.
– If non-homogeneous system, determine worst case. (coldest food
temp, shortest time, lowest Aw)
5
D & z Values Defined
- D-value: the time required to reduce by 90% (1 log cycle) the population of the target microorganism at a reference temperature
- z-value: the number of degrees of temperature required for the TDT curve to traverse one log cycle
Example:
(D185°F = 10.0 minutes, z = 10 F°). z=(T2-T1)/(logD1-D2)
6
• Used in conventional thermal processing since 1920
• Relies on heat-penetration data and TDT data
1. Lethal rate:
2. Lethality:
3. Cumulative log reduction:
D = # min required for 1-log (90%) reduction of the target organism
t = time (min)
T = actual temperature in the heat penetration study
Tref = reference temperature of the TDT study
z = # of degrees (C° or F°) for TDT results to move 1 log cycle.
Lethality = L · Δt
10 (T – Tref)/z
Σ [( )Δt]/D or (L·Δt)/D
L = 10 (T – Tref)/z
General Method Calculation
Σ
7
• Weibull solved for n determines if shape
significantly different from linear.
• Linear distribution is special case of Weibull
where n=1.
– n>1 = underestimate time and temp needed.
– n=1 or n<1 a linear distribution is most
conservative estimate of TDT.
Express TDT in Weibull or Linear?
Linear:
dN/dt=-kt
N= # living cells after exposure at time t
k= rate constant
Weibull:
Log S = -btn
b= scale, n= shape
(when n=1, constant rate – linear)
8
Linear is Best Fit for TDTs
• short processing times
• Little to no curve
• Linear fit is less sensitive at ends/tails
Time
Log r
eduction
0.85 Aw 0.57 Aw 0.21 Aw
9
Thermal Death Time Test Results
• Of multiple strains tested, S. Tennessee
slightly more heat resistant than others,
thus S. Tennessee selected for model
(Lucore et al, 2011).
TDT within Cereal Food Form (Flour, Solute, Water)
Strain Aw z-value (°C) D Value (°C)
Salmonella Tennessee 0.96 6.9 10^(-0.1444T*+20.763)
Salmonella Tennessee 0.85 7.4 10^(-0.1346T*+10.543)
Salmonella Tennessee 0.57 10.8 10^(-0.0921T*+8.0944)
Salmonella Tennessee 0.21 11.5 10^(-0.0873T*+9.0586)
* T = Temp in C (e.g. D93C (200F) @ .21 Aw = 8.1min) 10
Another way of viewing TDT Results…
Temp (C) Temp (F) 0.21 0.57 0.85 0.96
27 80 5377742 434911 8988075 1613119966
38 100 576235 41216 287152 2083425
49 120 61745 3906 9174 2691
60 140 6616 370 293 3.48
71 160 709 35 9 0.00
82 180 76 3.32 0.30 0.00
93 200 8.14 0.32 0.01 0.00
104 220 0.87 0.03 0.00 0.00
116 240 0.09 0.00 0.00 0.00
D-Values at given Aw
11
D values of S. Tennessee in Cereal Food Forms at
Different Aw (NFL, 2010)
2.13
0.075 0.001 0
0.5
1
1.5
2
2.5
0 0.2 0.4 0.6 0.8 1
Aw
D v
alu
e (
min
ute
s)
Cereal dough
Finished cereal
Milled cereal
Thermal Death Time Studies
At 100C(212F), 2000
times longer to kill
the same population
when comparing .21
& .85 Aw food.
Date: _____________ PROCESS LETHALITY DETERMINATION
Organism: _Salmonella_______________
Product name: __Example_______ EXAMPLE: Lethality Data from Literature
Microbial
Heat
Tolerance
T ref z D
User Must: Organism Product ( ºF) ( ºF) (min)
1. Identify organism and product of concern Salmonella Meat Patty (Scott and Weddig, 1998) 150 10 0.172
2. Provide at least 20 time/temp data points Gr. Beef (25% fat) (Juneja, 2003) 140 46.51 19.31
E. coli O157:H7 Lean Gr. Beef (2% fat) (Line et al., 1991) 145 8.3 0.30
Instructions: Gr. Beef (25% fat) (Juneja, 2003) 140 43.39 20.90
1. Select the organism and product of concern Lean Gr. Turkey (Juneja and Marmer, 1999) 149 43.7 1.45
and identify corresponding T ref, z, and D Lean Gr. Lamb (Juneja and Marmer, 1999) 149 44.4 1.90
values in the table. These values should be Lean Gr. Pork (Juneja and Marmer, 1999) 149 43.7 1.60
obtained from your own companies challenge Listeria
Lean Gr. Beef (2% fat) (Fain, et al., 1991) 145 9.3 0.6
study data, from scientific literature, or other monocytogenes Gr. Beef (25% fat) (Juneja, 2003) 140 42.98 27.7
reliable sources. These values need to be Hot Dog Batter (30% fat) (Mazzotta and Gombas, 2001) 160 11 0.5
relevant and appropriate for the type of product Note: This model is a tool for calculating F-values. To ensure correct results, the proper z, T-ref, and
and the organism of concern. D-values for each product and organism must be used.
2. Enter the T ref, z, and D values into the
appropriate labeled cells below the table that z = 10 ºF T ref= 150 ºF
contains the lethality data from literature.
3. Clear and enter at least 20 time/temp data D = 0.172 min
points into the data table.
4. Once the table is completed, a cumulative
F Log Reduction
of Process = 116577.95
value will be given as the very last number in
the right hand column of the data table. This Data Table
number adds up the lethality values for each Time (min) Core Temp (ºF) F-value (min)
time interval and calculates an approximation 0 40 0.000
of the area under the lethal rate curve. 5 49 0.000
5. After the data is entered, a core 10 58 0.000
temperature and lethality curve are produced. 15 67 0.000
6. The total log reduction of the process is 20 76 0.000
automatically determined by dividing the 25 85 0.000
cumulative F value by the D value that was 30 94 0.000
entered into the appropriate labeled cell. The 35 103 0.000
resulting value equals the total log reduction 40 112 0.001
of the process. 45 121 0.004
7. By using these estimates, you or a process 50 130 0.032
authority should determine if the process 55 139 0.256
meets regulatory requirements as safe. 60 148 2.032
Additional documents, such as Appendix A, 65 157 16.139
which discuss desired log reductions should 70 166 128.195
also be considered when evaluating a lethality 75 175 1018.292
process. 80 184 8088.577
85 180 16868.293
90 170 19618.293
95 165 19947.350
100 160 20051.407
Model Structure: Modified Method
• Example of AMI* format: • Example of Modified format:
0
50
100
150
200
0 50 100 150
Tem
pe
ratu
re (
F)
Time (min)
Core temperature
0.000
10000.000
20000.000
30000.000
0 100 200
F-v
alu
e (
min
)
Time (min)
Lethality
Time (s) Aw Temp (ºc) D Log red
1 0.8 29.40 32366.97 0.00
10 0.76 43.30 2399.55 0.00
20 0.74 68.30 14.47 0.01
30 0.69 87.80 0.36 0.24
40 0.63 98.80 0.06 1.68
50 0.58 101.60 0.05 3.40
60 0.53 104.40 0.04 5.46
70 0.47 105.70 0.05 7.10
80 0.42 105.60 0.08 8.16
90 0.35 105.70 0.14 8.77
100 0.31 104.20 0.26 9.08
110 0.25 104.10 0.44 9.27
120 0.2 82.20 68.16 9.27
0.00
2.00
4.00
6.00
8.00
10.00
1
10
20
30
40
50
60
70
80
90
100
110
120
Cumulative Log Lethality Across Thermal Process
0.00 20.00 40.00 60.00 80.00
100.00 120.00
1
10
20
30
40
50
60
70
80
90
100
110
120
Change in Food Temp (C) Across Thermal
13
Using D-values
1. “Step-by-Step” = holding D value constant through
each Aw.
- Limitation - Likely to overestimate results.
HEAT STEP AFOOD
TEMP (°F/°C)
BDWELL
TIME
(MIN)
CWATER
ACTIVITY
IN
DWATER
ACTIVITY
OUT
EData Used
for D value
calculations
Dwell time at
Aw
FCalculated
Log D (Time in
minutes to = 1
log kill)
GD reduction
(# log reduced
based on dwell
time)
Ste
p 1
(w
et)
COOK
IN 80/27 45 0.58
aw = .58
Tr = 72
Dr = 45
z = 30.3
22.5 1375.2 0.0
COOK
OUT 225/107 45 0.91
aw = 0.89
Tr = 65.5
Dr = 4.8
z = 6.9
22.5 0.0 10431531.8
A The product/food temperature reported from the facility
B The dwell time within the heat step divided by 2 to split dwell time between entering and exiting water activity
C Entering water activity of food
D Exiting water activity of food
E Reference Information Collected from Archer et al., 1998 and Sumner et al. 1991, respectively
F Time in minutes to enable 1 log kill at temperature and water activity reported
G # of Logs of kill achieved based on reported dwell time within the reported termperature and water activity
Log D = Log (DR)+(TR-TA)/ZR
14
Time
Log r
eduction
0.85 Aw 0.57 Aw
0.21 Aw
Using D-values
2. Regression = use TDT results to create prediction
formula and “smooth” the results across the thermal
process.
Using regression from results of
individual TDTs: Change in D-value at a
given temperature as Aw changes.
15
Using D-values
2. Regression
- Limitation – curve may extrapolate incorrectly at
temperatures that exceed the parameters of the TDT
tests.
5 log
212F/100C
.8 Aw .5 Aw .2 Aw
Time
16
Effect of Different D-Value Options
Time (s) Aw Core Temp D Regression
10 0.80 85F (29.4C) 33175.11 0.000
20 0.74 110F (43.3C) 2771.42 0.000
30 0.69 155F (68.3C) 22.18 0.008
40 0.63 190F (87.8C) 0.57 0.298
50 0.58 210F (98.8C) 0.09 2.233
60 0.53 215F (101.6C) 0.08 4.452
70 0.47 220F (104.4C) 0.07 6.998
80 0.42 220F (104.4C) 0.10 8.622
90 0.36 215F (101.6C) 0.29 9.199
100 0.31 210F (98.9C) 0.81 9.404
110 0.25 200F (93.3C) 4.12 9.444
120 0.20 180F (82.2C) 67.84 9.447
Time (s) Aw Core Temp D Step-by-step
10 0.80 85F (29.4C) 559113.53 0.000
20 0.74 110F (43.3C) 20183.66 0.000
30 0.69 155F (68.3C) 87.10 0.002
40 0.63 190F (87.8C) 0.81 0.207
50 0.58 210F (98.8C) 0.06 3.171
60 0.53 215F (101.6C) 0.03 8.950
70 0.47 220F (104.4C) 0.01 20.218
80 0.42 220F (104.4C) 0.01 31.486
90 0.36 215F (101.6C) 0.03 37.265
100 0.31 210F (98.9C) 0.06 40.229
110 0.25 200F (93.3C) 0.21 41.008
120 0.20 180F (82.2C) 3.09 41.062
* Regression is considered best case as it calculates a more conservative
result across changing Aw and temperature.
* Representative Time & Temp
Regression determination
of D-Value
Step-by-Step determination
of D-Value
17
Suggested Best Practice for Use of D-value
3. “Controlled Regression” = prediction formula
holds values constant beyond parameters of
lab TDTs to prevent over-estimation of
lethality.
- Additionally, if results exceed 10 log, report as >10
as is difficult to accurately quantify higher results . 0
.80
0.6
9
0.5
8
0.4
7
0.3
6
87.78
98.890.000
2.000
4.000
6.000
8.000
10.000
12.000
Lo
g K
ill
Aw
Temp (C)
Regression of all
linear TDT results to
address changing
Aw across process
time
18
Effect to Model of Precise Aw Data
• Linear reduction between points of measurement
provides most conservative result.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 min 2 min 4 min 6 min 8 min
Food A
Food B
Food C
19
Effect to Model of Precise Aw Data
• Additional data points collected improves precision.
Time (s) Aw Core Temp Lethality
10 0.80 85F (29.4C) 0.000
20 0.74 110F (43.3C) 0.000
30 0.69 155F (68.3C) 0.008
40 0.63 190F (87.8C) 0.298
50 0.58 210F (98.8C) 2.233
60 0.53 215F (101.6C) 4.452
70 0.47 220F (104.4C) 6.998
80 0.42 220F (104.4C) 8.622
90 0.36 215F (101.6C) 9.199
100 0.31 210F (98.9C) 9.404
110 0.25 200F (93.3C) 9.444
120 0.20 180F (82.2C) 9.447
Time (s) Aw Core Temp Lethality
10 0.80 85F (29.4C) 0.000
20 0.80 110F (43.3C) 0.000
30 0.79 155F (68.3C) 0.018
40 0.75 190F (87.8C) 0.781
50 0.71 210F (98.8C) 6.500
60 0.68 215F (101.6C) 14.511
70 0.57 220F (104.4C) 20.288
80 0.46 220F (104.4C) 22.605
90 0.36 215F (101.6C) 23.182
100 0.31 210F (98.9C) 23.387
110 0.25 200F (93.3C) 23.427
120 0.20 180F (82.2C) 23.430
Linear Decline
between measured
Aw Points
20
Baking process is
divided into
4 segments.
TDT study data is
applied within
each segment.
D-values increase
through the
process.
Lethality during
cooling is not
calculated.
Example of a Log-Reduction Calculation
21
Segment Time (min) Aw Temp (ºC/ºF) D - Value Log red
1 1 0.75 65/150 26.73 0.04
2 1 0.74 85/175 0.43 2.35
3 1 0.35 100/195 0.46 2.18
4 1 0.21 110/215 0.18 5.64
Total 10.20
Mathematical Modeling
2.4 2.2 5.6 Log reduction:
22
Summary of Model Recommendations
• Test multiple strains of target organism – Best choice should be selected from those found within
food form or ingredients within food form.
• Maintain stable Aw during TDT tests within appropriate food form. – Provides multiple “snapshots” of thermal effect at each
Aw sampled in process.
• Calculate lethality using “Controlled Regression” to create most conservative result.
• Linear reduction of Aw between each measured Aw point of process.
• Worst case process variables selected for calculating lethality in thermal system
23
References
• American Meat Institute Foundation. 2010. AMI Foundation Process
Lethality Determination Spreadsheet – Aug 2010
http://amif.org/ht/d/sp/i/26870/pid/26870/
• Anderson, D. G. and L. A. Lucore. 2012. Validating the Reduction of
Salmonella and Other Pathogens in Heat Processed Low-Moisture Foods.
Alliance for Innovation & Operational Excellence, Alexandria, VA.
Published online at http://community.pmmi.org/Alliance/Home/. Accessed
[scheduled for April, 2012].
• Archer J., E. T. Jervis, J. Bird, and J. E. Gaze, 1998. Heat resistance of
Salmonella weltevreden in low-moisture environments. J. Food Prot. 61:
969-973.
• GMA, 2011. Summary of GMA Scientific and Regulatory Affairs Projects
2010, "Thermal Inactivation and Survival of Salmonella in Food as a
Function of Water Activity and Fat Level". p. 8. Grocery Manufacturers
Association, 1350 I Street, NW, Suite 300, Washington, DC 20005.
www.gmaonline.org
24
References • Goepfert, J. M., and R. A. Biggie. 1968. Heat resistance of Salmonella
typhimurium and Salmonella senftenberg 775W in milk chocolate. Applied
Microbiology 16:1939-1940.
• Liu, T. S., G. H. Snoeyenbos, and V. L. Carlson. 1969. Thermal resistance
of Salmonella senftenberg 775W in dry animal feeds. Avian Diseases
13:611-631.
• Lucore, L. A; M. A. Moorman, B. L. S. Jackson, 2011. Lethality Validation
of “Thoroughly Cooked” Products: A Dry Foods Toolbox. IAFP annual
meeting, August 2, 2011. Milwaukee, WI.
• Marks, B.P., J. Tang, E. T. Ryser, S. Wang and S. Jeong, project
directors. 2011. Improving process validation methods for multiple
pasteurization technologies applied to low-moisture foods. USDA project
number MICL05056. Biosystems & Agricultural Engineering, Michigan
State University, East Lansing, MI 48824.
• Sumner, S., T. M. Sandros, M. C. Harmon, V. N. Scott, and D. T. Bernard.
1991. Heat resistance of Salmonella typhimurium and Listeria
monocytogenes in sucrose solutions of various water activities. J. Food
Sci. 6:1741-1743.
25
Thank You
Lisa Lucore, Ph.D. Food Safety Scientist
The Kellogg Company
26
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