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

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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

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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

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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.

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Thank You

Lisa Lucore, Ph.D. Food Safety Scientist

The Kellogg Company

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