Comparative Risk Assessment 

for Intact (Non-Tenderized) 

and 

Non-Intact (Tenderized) Beef: 

Technical Report 

Prepared by: 

Risk Assessment Division 

Office of Service 

United States Department of Agriculture 

Public Health Science 

Food Safety and Inspection 

March, 2002
Technical Report: Comparative Risk Assessment For Intact And Non-Intact (Tenderized) Beef March 18, 2002 

1. 

1.1. 

INTRODUCTION, HISTORY OF THE PROBLEM, AND BACKGROUND 

INFORMATION ON STEAK AND ROAST PRODUCTION 

E. coli O157:H7, a Shiga toxin-producing E. coli, is a recognized human pathogen. A draft risk 

assessment of the public health impact from E. coli O157:H7 in ground beef (ECRA, 2001) is 

currently under review by the public and the National Academies of Sciences. That assessment 

quantifies the risk to public health from E. coli O157:H7 in ground beef. 

Since 1994, FSIS has deemed ground beef contaminated with E. coli O157:H7 to be adulterated. 

Yet, the National Advisory Committee on Microbiologic Criteria for Foods (NACMCF) 

determined in 1997 that “due to the low probability of pathogenic organisms being present in or 

migrating from the external surface to the interior of beef muscle, cuts of intact muscle (steaks) 

should be safe if the external surfaces are exposed to temperatures sufficient to effect a cooked 

color change.” Therefore, intact steaks and roasts contaminated with E. coli O157:H7 have not 

been deemed adulterated. 

On January 19, 1999, FSIS published a Federal Register notice explaining that, in addition to 

ground beef, raw non-intact beef products (e.g., beef that has been mechanically tenderized by 

needling or cubing) that are found to be contaminated with E. coli O157:H7 must be processed 

into ready-to-eat product, or they would be deemed adulterated. 

The physical process of tenderization is an important reason for FSIS concern. Mechanical 

tenderization translocates dangerous pathogens from the surface of intact beef cuts to beneath 

the surface thereby potentially shielding those pathogens from the lethal effects of heat during 

cooking. 

Process flow for Steak Production 

After cattle are slaughtered, their carcasses are processed and usually chilled. Eventually, the 

carcasses undergo a fabrication step wherein primal and sub-primal cuts of meat are generated. 

Fabrication is a process that involves the use of knives to trim carcasses into smaller subunits of 

muscle tissue. The first division of the carcass is into primal cuts. These cuts are further 

subdivided into subprimal cuts (Figure TR 1 and Figure TR 2). Fat, defects, and most bones are 

removed during trimming. Byproducts of fabrication are beef trimmings that are used to make 

ground beef. A single serving of ground beef is a mixture of trim from many carcasses. In 

contrast, a single serving of steak or roast is from a single carcass. 

Following fabrication, subprimal cuts can be mechanically tenderized. Tenderization involves 

repeated penetration of the muscle surface to disrupt muscle fibers and render the muscle more 

tender. The effect of these physical tenderization processes is not visible to the naked eye. 

Following tenderization, the subprimal piece is usually further cut into steaks or roasts. 

2 
Tenderization can move pathogens on the surfaces of subprimal cuts to beneath the surfaces. 

Cutting steaks from a subprimal can also move pathogens from the surface to the freshly cut 

surface. If we consider a tenderized steak that is a cross-section of a subprimal piece, then the 

edges of the steak may be contaminated from the original contamination on the surface of the 

subprimal, the steak’s surface may be contaminated from the knife cutting to create the steak, 

and the interior may be contaminated from the tenderization process. 

Fabrication Primal/subprimals 

for steaks/roasts

Tenderized

(optional)

Steaks cut Storage and

handling Cooking

Consumption

Live cattle Knock box 

stunning Dehiding Carcass decontamination

Evisceration Carcass

splitting

Carcass

washing Chiller

Trim for 

ground beef

B A

Figure TR 1. Flow diagram of steaks and roasts from slaughter to consumption. 

3 
Figure TR 2. American Angus Association’s description of beef cuts. 

1.2. Mechanical Tenderization 

Mechanical tenderization is performed using stainless steel blades or needles. The needles or 

blades are capable of penetrating the meat by cutting through muscle tissues and fibers, rather 

than tearing the tissue or punching holes. Blade tenderization refers to a series of sharp stainless 

steel, double-edged blades, 3 mm in width, that pierce the meat. The blades are oriented at right 

angles with each other in a pattern of 5 blades per square centimeter (Ross Industries, 1998, 

used in Sporing, 1999). This results in a distance of about 5 mm between parallel blades. 

One type of needle tenderization uses 3mm diameter stainless-steel needles to pierce a 

subprimal in a similar manner to the blades. The distance between needles is 5 mm. Two heads 

of needles alternately pierce a subprimal piece of beef. The heads are mounted at a 45° angle to 

either side of a plumb line. A conveyor belt advances the subprimal 38mm (1.5 inches) between 

each stroke of the tenderizer. Each needle head contains 268 needles on a surface that is 76mm 

by 280mm (3 by 11 inches). Therefore, 1.26 needles puncture each cm2 of meat surface each 

time the needle head strikes. It is estimated that each piece of meat is pierced about 3 times. 

4 
1.3. 

1.4. 

1.5. 

The Mechanism of Cross-contamination 

While blade or needle tenderization can internally contaminate a subprimal whose surface is 

contaminated, those processes may also cross-contaminate subprimals that are not 

contaminated. As a blade or needle passes through a contaminated surface, E. coli O157:H7 is 

carried deeper into the muscle tissue. Presumably, there is some initial transfer of surface E. 

coli O157:H7 onto the blade followed by a transfer from the blade to the internal muscle tissues. 

In work done by Sporing (1999), blade tenderization carried E. coli O157:H7 from the top 

surface to a depth of 6 cm. Yet, the amount of E. coli O157:H7 translocated decreased with 

increasing depth of penetration. For each cm of penetration from the inoculated surface, there 

was about a 0.5 log decrease in the amount of E. coli O157:H7 detected. If organisms are 

transferred from the blade to muscle from a frictional mechanism that is constant throughout the 

muscle tissue, then the lower numbers of organisms at greater depths are best explained as a 

result of fewer organisms available on the blade (i.e., with increasing depth there are fewer 

organisms to transfer). If true, this mechanism suggests there are few organisms remaining on a 

blade after it is used on a contaminated subprimal. Furthermore, when that blade encounters the 

next subprimal, the same mechanism suggests that most organisms will be deposited near the 

surface of that subprimal. Consequently, cross-contamination may be a less hazardous outcome 

from tenderization than the direct effect of translocating E. coli O157:H7 beneath the surface of 

a contaminated cut of meat. 

Consumption 

Given that most steaks and roasts consumed in the U.S. originate from steer/heifer carcasses, we 

can get a rough estimate of the total number of such servings annually consumed. Assume that 

roughly 27 million steers and heifers are slaughtered annually. A typical carcass is 750 lbs. but 

30% of that weight is fat, bone, and shrinkage. So, there are about 500 lbs. available for retail 

cuts of beef. Roughly one-third of retail cuts are in the form of ground beef and stew meat. The 

remaining two-thirds are equally divided between steaks and roasts. Therefore, about 330 lbs. 

of steaks and roasts are harvested from such a typical carcass. If we assume that a typical 

serving size is about a quarter pound across the entire population of consumers, then there are 

about 36 billion servings of steak and roast consumed annually (i.e., 27 million carcasses X 330 

lbs./carcass X 4 servings/pound). 

Roasts versus steaks 

Roasts and steaks are cut from the same primal cuts of beef. Roasts are multiple serving cuts, 

while steaks are usually fabricated to be single servings. Roasts are usually thicker than steaks 

when cooked. Thickness can influence the rate at which cooking kills E. coli O157:H7 inside 

muscle tissue. Yet, because of their thickness, roasts are usually cooked for a longer time than 

steaks. A conclusion from a cooking study (Sporing 1999) was that thicker cuts of steak (3.2 

cm vs. 1.3 cm) resulted in more effective log reductions when cooked to the same target internal 

temperature. The time needed to achieve a target internal temperature necessarily increased 

with thickness. Longer cooking times subjected the more superficial layers of the roast to 

higher temperatures for longer periods. Greater log reductions in contamination resulted. 

5 
Three cooking methods were evaluated by Sporing (1999): broiling, grilling, and skillet frying. 

These are typical methods for cooking steaks. Nevertheless, roasts are usually cooked by 

braising or roasting. Of the three methods evaluated by Sporing (1999), broiling seems to be the 

best surrogate for braising or roasting. As will be discussed later, both skillet frying and grilling 

were shown to be less effective cooking methods for eliminating E. coli O157:H7 bacteria than 

broiling. Therefore, we will focus on the hazard of tenderization for steaks but assume that this 

hazard is similar, or possibly less, for roasts. 

1.6. 

2. 

Purpose 

The purpose of the analysis to be described below is to compare the risk to human health from 

E. coli O157:H7 in blade-tenderized steaks to that from E. coli O157:H7 in steaks that have not 

been tenderized. We do this by calculating the difference in the frequency of human illness 

caused by tenderized and non tenderized steaks. We assume that the risk from tenderized roasts 

is similar to, or less than, the risk from steaks. We also assume that blade tenderization is a 

valid surrogate for any mechanical tenderizer. 

Non-tenderized steaks are considered ‘safe’ if cooked to effect a color change. Therefore, if 

such steaks are contaminated with E. coli O157:H7, they are not considered adulterated. The 

concern with tenderized steaks is that they may not be safe in comparison to non-tenderized 

steaks. This analysis should be useful in policy-making and serve as a tool for measuring the 

potential public health benefits of regulatory options regarding mechanical tenderization 

(including the option not to regulate). 

ASSUMPTIONS AND METHODS 

The model to be described below is based on the following modeling assumptions: 

a) Meat is normally sterile beneath exposed surfaces, but some surface contamination with 

pathogens can occur. Therefore, intact beef (i.e., non-tenderized) can be contaminated 

on the exposed surfaces with E. coli O157:H7, but such contamination does not occur 

beneath the surface. During storage and handling of meat, E. coli O157:H7 levels can 

increase. Growth of E. coli O157:H7 on the surface or beneath the surface of steaks is 

similar. Assuming no differential growth rates allows growth of E. coli O157:H7 to be 

modeled as independent of the tenderization step. 

b) Lethality of cooking is a function of the cooking temperature, method of cooking, time 

of cooking, and thickness of the meat. Lethality can be measured in log reductions of E. 

coli O157:H7 achieved. Increased cooking temperature always increases lethality if all 

other factors remain unchanged. Furthermore, there exists a temperature at which an 

infinite log reduction occurs (i.e. at which all bacteria are killed). 

c) One E. coli O157:H7 organism contains all that is necessary to result in infection and 

illness if consumed. The likelihood of infection and illness is dose-dependent. 

6 
d) The results of blade tenderization experiments by Sporing (1999) reasonably apply to 

other methods of mechanical tenderization. 

e) The practice of cooking roasts is reasonably similar to cooking of steaks. 

f) The level of translocation noted by Sporing (1999) (i.e., ~4% of surface E. coli

O157:H7), is reasonably indicative of the results of commercial tenderization. 

g) The effect of cooking on specific thicknesses of steaks is similar to the effect of cooking 

on similar thicknesses. Thus, thicknesses of 1.3 cm, 1.9 cm, and 3.2 cm represent all 

steak thicknesses. 

h) Not-tenderized and tenderized steaks are all stored, prepared, and cooked in identical 

conditions. This assumption allows focusing on the specific differences in risk between 

not-tenderized and tenderized steaks. 

The following sections describe, using illustrative examples and minimal mathematical notation, 

the 9 steps involved in the analytical framework. These steps involve discussions on: 

1. The issue described 

2. The computation process 

3. Bacteria in servings before cooking. 

4. The attenuation factor 

5. Bacteria in servings after cooking 

6. Uncertainty about variability 

7. Cooking attenuation diagrams 

8. Cooking input/output relationship 

9. The Dose response relationship 

2.1. The issue described 

The majority of steaks and roasts destined for hotel, restaurant, and institutional use in the 

United States may be subjected to “mechanical tenderization.” This is a process in which large 

pieces of meat are penetrated, usually in several directions, by sets of needles, or double-edged 

blades, and then cut into steaks and roasts. The purpose of the process is to break up the meat’s 

structure and make it more tender. Sometimes the needles used are hollow, so that through the 

holes the meat can be injected with solution containing flavorings and/or digestive agents such 

as papain. Sometimes the injected solution is recycled. There are three items of concern in 

connection with this process. 

 

1. If there are bacteria (especially E. coli O157:H7) on the surface of the meat, the 

tenderizing process may transfer some of these organisms to the interior of the meat 

where they may be shielded to some extent from the heat of the cooking process. 

Thus some of these may survive a cooking process that would kill all bacteria on the 

surface. 

7 
2. Unless the needles are sanitized after each piece of meat they could carry bacteria 

from a contaminated piece to subsequent piece that were previously uncontaminated. 

 

3. If the injected fluid is recycled, this constitutes another mechanism by which bacteria 

from a contaminated piece could be transferred to previously uncontaminated piece. 

If we envision a series of meat pieces coming down a conveyor to the tenderizer then some 

fraction of those pieces would be contaminated and some not. The effect of items (2) and (3) 

then would be that in the series of pieces downstream of the tenderizer the fraction contaminated 

would be somewhat increased. Most worrisome, however, is item (1) because it suggests that 

some of the bacteria in the final product may survive the cooking process, and may thus cause 

illness in the consumer. 

The purpose of this report is to compare the risk of E. coli O157:H7 illness from steak servings 

that have been prepared from tenderized product to the risk from product that has not been 

tenderized. For this purpose we adopt the following computational process: 

Degree of contamination before cooking. 

This is expressed by a variability histogram of BPSBC (bacteria per serving before cooking) 

(Figure TR 3) 

0.0000001

0.000001

0.00001

0.0001

0.001

0.01

0.1

1

0 1 10 100 1000 10000 100000

BPSBC

Fraction of servings exceeding

Figure TR 3. Diagram of the variability in E. coli O157:H7 organisms per serving before

cooking (BPSBC). This is an exceedance diagram and shows the fraction of servings exceeding 

the BPSBC. 

2.1.1. Degree of attenuation during cooking 

This is expressed by Attenuation Factor diagrams, with and without tenderizing (Figure TR 4). 

8 
Figure TR 4. Example attenuation factor diagrams for beef that is not-tenderized or tenderized, 

and cooked by broiling, grilling, or frying 

Intact Beef (IB), non-tenderized

1.E-09

1.E-08

1.E-07

1.E-06

1.E-05

1.E-04

1.E-03

1.E-02

1.E-01

1.E+00

45.0 50.0 55.0 60.0 65.0 70.0 75.0 80.0

CT=Cooking Temperature (C)

Attenuation factor

FitBroiling FitGrilling FitFrying

Cooking method

(CM)

Non-intact Beef (NIB), tenderized

1.E-09

1.E-08

1.E-07

1.E-06

1.E-05

1.E-04

1.E-03

1.E-02

1.E-01

1.E+00

45.0 50.0 55.0 60.0 65.0 70.0 75.0 80.0

CT=Cooking Temperature (C)

Attenuation factor

FitBroiling FitGrilling FitFrying

CM

2.1.2. 

2.1.3. 

2.2. 

Degree of contamination after cooking 

Contamination after cooking is calculated by multiplication 

eqn (1) (bacteria per serving after cooking) = BPSAC= BPSBC x AF 

where AF (attenuation factor) is a function of: 

1. CT, cooking temperature 

2. CM, cooking method 

3. ST, steak thickness 

4. T or NT, intact or non-intact 

Illnesses resulting from that contamination. 

BPSAC can be converted to Illnesses per Serving (IPS) by passing BPSAC through the DoseResponse Curve [DRC(dose)]. 

eqn (2) IPS = DRC(BPSBC x AF(CM, CT, ST, T or NT)) 

 

The effect of tenderization is shown by changing the variable T to NT in this equation. 

Computation process 

For point estimate: 

Choose BPSBC, CM, CT, ST, T or NT 

Find AF(CM, CT, ST, T or NT) from the attenuation curves. 

9 
Calculate BPSAC = BPSBC x AF 

Calculate IPS = DRC(BPSAC) 

Our output quantity, IPS, is thus a function of five variables: 

BPSBC (input variable) 

CM (parameter) 

CT (parameter) 

ST (parameter) 

T or NT (parameter) 

The cooking effect for T or NT is shown by comparing the two sets of curves in section 2.1.2. 

2.3. Bacteria per serving before cooking (BPSBC) 

The following conceptual model reflects the relevant stages in the farm-to-table process where 

E. coli O157:H7 is present (and could potentially grow) in tenderized and nontenderized beef 

products (Figure TR 5). 

Fabrication Primal/subprimals Contaminated

Not-contaminated

Tenderized

(optional)

Steaks cut Contaminated

serving

Not-contaminated

serving

Storage and

handling Cooking

Consumption

B A

Figure TR 5. Conceptual model of E. coli O157:H7 contamination on steaks. 

This conceptual model describes the movement of product from the fabrication step in slaughter 

through the generation of primal and subprimal cuts of beef, tenderization, creation of steak 

servings, storage and handling, cooking, and consumption. Ideally, we could describe 

input/output relationships at each of the steps shown here, but currently, there is no sampling 

10
data on the prevalence and levels of E. coli O157:H7 on, or in, steaks and roasts. Therefore, we 

have chosen to begin our consideration of the bacteria on servings at point A in this figure. 

Assuming we know the number of bacteria on steaks at Point A, then Point B describes the 

output from a series of steps wherein steaks are transported, stored, and prepared prior to 

cooking. Each step of storage and handling (e.g., storage prior to shipment, storage at retail, 

etc.) can be characterized by the relationship between the BPS at the input to the step and the 

BPS at the output. If storage and handling of steaks is properly done, the BPSout equals the 

BPSin (i.e., there is no growth of E. coli O157:H7). If time and temperature conditions are such 

as to enable E. coli O157:H7 to grow, then BPSout> BPSin. The growth factor, G, between 

points A and B, is defined as; 

eqn (3) 

 in

out

BPS

BPS

BP at point A

G

S

BPS at point B = =

where G is a function of storage time, storage temperature, and growth parameters specific to E. 

coli O157:H7. If we collapse all the steps comprising point B into a single node, we can 

generate a diagram for this input/output stage (Figure TR 6). 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.2 0.4 0.6 0.8 1

BPSin

BPSout

Temperature

Time

Figure TR 6. Example of input-output diagram for bacteria per serving at a single node. 

Because storage time and temperature are variable among the population, then so also the 

growth ratio G is similarly variable. We describe this variability with a “Discrete Frequency 

Distribution” (Kaplan 1981, Kaplan 1987) for the growth parameter, G 

eqn (4) G = {<FGi; Gi>}. 

Here FGi is the fraction of servings that experience growth factor Gi. 

Now define: 

11
BPSin = the number of bacteria on steaks at point A, 

BPSout = the number of bacteria on steaks at point B, just before cooking (i.e., this is also 

BPSBC). 

Since the quantity BPSin A is variable we describe that variability with a discrete frequency 

distribution: 

eqn (5) BPSin = {< FBPSin, k; BPSin, k >} 

Therefore, from eqn (4) eqn (5) , 

eqn (6) BPSBC = {<FBPSBCk,i, BPSBCk,i >} = {< FBPSin,k x FGi ; BPSin,k xGi >} 

Figure TR 3 shows the curve for BPSBC calculated according to eqn (6) . 

2.4. 

2.5. 

Attenuation factor, AF 

The factor AF defines the I/O relationship for cooking. That is, it relates BPSBC to 

BPSAC according to the relationship:. 

eqn (7) BPSAC = BPSBC x AF 

AF is a function of four parameters: the cooking temperature, CT, cooking method, CM, Steak 

thickness, T, and tenderized or not, T/NT. 

eqn (8) AF = AF(CT, CM, T, T/NT ). 

So, given values for each of the four parameters we can calculate an AF and using this in eqn 

(7) we can calculate a point estimate because,

eqn (9) BPSBC → BPSAC → DRC → Illnesses per serving (IPS) 

Thus, given an input BPSBC, we can calculate IPS as a function of CT. 

Uncertainty about a variability distribution 

As an example of a variability distribution in the model we consider BPSBC. This variability in 

the in the level of contamination in the incoming steak population (i.e. before cooking) was 

expressed as a curve (Figure TR 3). Call this curve V. To express our uncertainty about this 

variability curve, V, we use a discrete probability density format. 

eqn (10) V = { <Vi, Pi>} 

12
i.e., put forth a set , {Vi}, of possible V curves and assign each a probability, Pi , such that the 

sum of all Pi is one. 

For simplicity we select three possible curves for V. We choose V2 as the “most likely” curve 

that represents our best guess about the true variability of bacteria per serving in the population 

of steaks under consideration. We chooseV1 as a “lower-bound” curve that is shifted leftward 

from V2, expressing a general reduction of bacteria per serving in the population. Similarly we 

choose V3 as an “upper bound” curve that suggests a higher frequency of larger numbers of 

bacteria per serving. We next assign probability values to these curves, for example P1 = P3 = 

0.1 and P2 = 0.8 (for a more detailed treatment, if desired, we could include more curves for V 

that lie between V1 and V3, but the three curves described are a simple approach that 

communicates the degree of uncertainty). 

Now we can calculate a new IPS with each of these curves as inputs for BPSAC. The range of 

results so calculated describes our uncertainty in IPS given our uncertainty about V. This 

analysis is conditioned on the other model inputs being set to their most likely values. 

We can perform similar analyses for other uncertain inputs to the model. For example, the 

curves for AF(CM, ST, CT) can be represented as sets of curves to represent our uncertainty in 

the parameter AF. 

2.6. Cooking attenuation 

Thus far, we have generated diagrams such as in Figure TR 4 to express the effect of cooking. 

We have generated such diagrams for each combination of steak thickness and tenderized or 

non-tenderized. The attenuation factor, AF, is defined as; 

BC

AC

BPS

BPS eqn (11) AF =

 

If we know the frequency of the various cooking methods (CM) and cooking temperatures (CT), 

then we can condense the attenuation diagrams to a single variability curve for AF. 

Denote: 

eqn (12) FCM = {<FCMF, frying>, <FCMG, grilling>, <FCMB, broiling>} 

where: 

FCMF is the fraction of steaks that are fried, 

FCMG is the fraction of steaks that are grilled, 

FCMB is the fraction of steaks that are broiled, 

and FCMF + FCMG + FCMB = 1.0 

Denote: 

13
eqn (13) FCT = {<FCTi, CTi>} 

where: 

FCTi is the fraction of steaks cooked at temperature CTi, and ∑ = .1 0

i

FCTi

Then we can calculate AF(CMj, CTi) occurs with frequency FCMj x FCTi because the pair 

(CMj, CTi) defines a cooking condition and, through the attenuation diagram, an attenuation 

factor, AFji. 

Thus, AF reduces to the discrete frequency distribution for AF as; 

eqn (14) AF = {<FAFj,i, AFj,i>} 

where FAFj,i = FCMj x FCTi and AFj,i is the attenuation factor for cooking by method j at 

temperature CTi. 

This discrete frequency distribution expresses the variability of the attenuation factor AF. If we 

combine (multiply) this with a variability curve for BPSBC, we will get a variability curve for 

BPSAC. 

2.7. 

2.8. 

Input/output relationship for cooking 

We now have a variability distribution for the contamination level before cooking, eqn (6) . 

We can combine this discrete frequency distribution with that for AF to get a distribution for 

contamination level after cooking, eqn (7) . Thus: 

eqn (15) BPSAC = {<FBPSACn; BPSACn>} 

where FBPSACn = FBPSBCk x FAFij and BPSACn = BPSBCk x AFji

Therefore, we have a method for converting a variability curve for BPSBC to a variability curve 

for BPSAC. This curve includes the variability of cooking temperature and cooking method. We 

can extend this method to also include the variability of steak thickness. 

Putting the BPSAC curve through a dose response relationship 

eqn (15) gives us a discrete frequency distribution for bacteria per serving after cooking. If 

we pass each value of BPSACn through a dose response curve (Figure TR 7), we get a 

corresponding illness per serving, IPSn. So, passing a whole discrete frequency distribution, 

BPSAC, though the dose-response curve, we get a discrete frequency distribution for IPS. 

eqn (16) IPS = {< FBPSACn ; IPS(BPSACn)>}. 

14
0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.2 0.4 0.6 0.8 1

BPSAC = Dose

IPS=Response

BPSACn

IPSn

Figure TR 7. Dose response relationship 

The IPS curve can be used as to summarize the results of this study. Thus, we can calculate the 

expected value of this curve to determine the expected illnesses per serving (EIPS) for steaks 

that are tenderized or not tenderized. 

eqn (17) IPSEV = EV{DRC(BPSAC)}= 

( ) ACn n

∑ DRC BPS ACn × FBPS

3. 

3.1. 

3.1.1. 

Model Inputs 

Organisms per serving before cooking, BPSBC 

Currently, there is no sampling data on the prevalence and levels of E. coli O157:H7 on, or 

in, steaks and roasts. To motivate an estimate, therefore, consider the conceptual model 

shown in Figure TR 5. This conceptual model describes the movement of product from the 

fabrication step in slaughter through the generation of primal and subprimal cuts of beef, 

tenderization, creation of steak servings, storage and handling, cooking, and consumption. 

Ideally, we could describe input/output relationships at each of the steps shown here, but we 

lack the data. Therefore, as noted before, we have chosen to begin our consideration of the 

organisms on servings at point A in this figure. We call this variable BPSin, eqn (5) . 

Initial organisms on serving before storage and handling, BPSin

As a surrogate for the number of organisms on a steak just after it is produced (BPSin), we 

modify the prediction from a risk assessment of E. coli O157:H7 in ground beef (ECRA 2001). 

Those predictions must be modified because ground beef is far from an ideal surrogate for 

steaks or roasts. Ground beef is generated from beef trim that typically comes from the surfaces 

of multiple carcasses. Given that E. coli O157:H7 resides on the surface of carcasses, it is likely 

that ground beef would be more contaminated than the primal/subprimal cuts of intact beef 

generated during fabrication. For example, a survey of raw ground beef found the average 

15
density of generic E. coli (Biotype I) was 1.73 logs per gram (FSIS, 1996). Jericho (2000) 

enumerated generic E. coli counts on various cuts of beef just before, and soon after, 

commercial packaging in a large slaughter plant in Canada (Table TR 1). The average density 

of generic E. coli found on these cuts was 0.53 logs per cm2

. This comparison suggests that 

levels of bacteria on steaks and roasts are generally less than levels in ground beef. 

Table TR 1. Generic E. coli results from sampling subprimal cuts of beef in a Canadian 

slaughter plant (Jericho 2000). 

Cut of meat Average Log(E. coli)/cm2 Standard error (16 df)

BCB – beef chuck boneless 0.36 0.11 

SCC – short cut clod 0.07 0.11 

BBB – beef brisket boneless 0.80 0.18 

SRT – short ribs full cut 0.03 0.18 

OSB – outside skirts boneless 0.11 0.09 

RBE – round boneless-eye 0.33 0.09 

SB – sirloin boneless 1.62 0.16 

TB – tenderloin boneless 0.53 0.16 

CBP – chuck boneless pectoral 0.16 0.16 

RBC – round boneless center bone out 1.42 0.16 

RBB – ribs boneless blade 0.02 0.09 

RBP – round boneless peeled 0.96 0.09 

Table TR 1 implies that levels of generic E. coli are relatively low on subprimal cuts of beef. 

Nevertheless, these data do not specifically address E. coli O157:H7. Therefore, we need an 

alternative approach to estimate BPSin. Our best estimate of the levels of E. coli O157:H7 in 

ground beef comes from the draft risk assessment of E. coli O157:H7 (ECRA 2001). That risk 

assessment predicts: 

• between 0.2% and 0.5% of ground beef servings are contaminated with one or more 

E. coli O157:H7 (Table TR 2), 

• 75% of a steer/heifer carcasses’ original surface area becomes beef trim used to 

make ground beef, 

• 25% of the original surface area’s E. coli O157:H7 remain to contaminate primal 

cuts of beef, 

• the remaining surface E. coli O157:H7 on primal cuts are distributed across about 

82% of the weight of the original carcass (i.e., 500 lbs. of beef per carcass and about 

18% is trim). 

Therefore, to estimate the fraction of contaminated steak/roast servings, we multiply the fraction 

of contaminated ground beef servings by a steak and roast adjustment factor. 

16
eqn (18) 

.07

.18

.75

.82

.25

bacteria per groundbeef serving

bacteria per steak serving SRAF

factor

adjustment

steak&roast

== ==

Table TR 2. E. coli O157:H7 levels predicted in ground beef servings before storage and 

handling from a risk assessment model (ECRA 2001). 

Bacteria per Fraction of servings 

serving Jun-Sep Oct-May 

0 99.5% 99.8% 

1 0.46% 0.19% 

3 0.038% 0.011% 

10 0.0035% 0.00040% 

31 0.0000027% 0.0000000002% 

100 0% 0% 

To adjust the ground beef predictions to model steaks and roasts we first determine the fraction 

of servings that would have no bacteria. We average the seasonal results to determine the 

annual fraction of servings containing no E. coli O157:H7: 

eqn (19) .0 997

12

8 .0 998

12

4 .0 995

0 bacteria

servings with

Ground beef

F =×+×=

⎟

⎟

⎟

⎠

⎞

⎜

⎜

⎜

⎝

⎛

The complement of eqn (19) is the fraction of contaminated servings. This is multiplied by 

SRAF, eqn (18) to determine the fraction of steaks and roasts that are contaminated. The 

complement of this is the fraction of steaks and roasts with 0 bacteria. 

eqn (20) F(BPSin=0) =1 - (1 – 0.997) x SRAF = 1 - (1 - 0.997) x 0.07 = 0.9998. 

Thus, 99.98% of steaks/roasts contain no E. coli O157:H7 (i.e., BPSin=0) and 0.02% of 

steaks/roasts contain one or more E. coli O157:H7 (i.e., BPSin>0). For contaminated servings 

(BPSin>0), the predictions in Table TR 2 show that contamination levels might vary from 1 to 

31 bacteria per serving. Yet, servings with 1 bacteria occur >10 times more frequently than 

servings containing 3 or 10 or 31 bacteria. To simplify the model, we assume BPSin can take 

only two values; 0 (for 99.98% of servings) or 1 (for 0.02% of servings). 

3.1.2. Growth multiplier from storage and handling, G 

If environmental conditions (e.g., storage temperatures) support growth of E. coli O157:H7, 

then bacteria in or on steaks/roasts may multiply between the time the steaks/roasts are 

produced/packaged and the time they are cooked. We have assumed the growth predictions 

17
from the E. coli O157:H7 in ground beef model (ECRA 2001) approximately apply to steaks 

and roasts (Table TR 3). These predictions suggest the following: 

• about 50% of servings have no change in the level of E. coli O157:H7 per serving, 

• about 49% of servings have a reduction in levels of E. coli O157:H7 resulting from 

frozen storage, 

• about 1% of servings are predicted to have some growth of E. coli O157:H7 

contamination during storage and handling. 

To simplify the model, we assume freezing eliminates any contamination on a steak or roast. 

Table TR 3. Logs of E. coli O157:H7 growth in ground beef as predicted by ECRA (2001). 

Logs of 

E. coli O157:H7 growth 

Fraction of 

servings 

-3.0 0.0136 

-2.5 0.0136 

-2.0 0.0815 

-1.5 0.2852 

-1.0 0.0951 

0.0 0.4997 

0.5 0.0030 

1.0 0.0029 

1.5 0.0015 

2.0 0.0009 

2.5 0.0006 

3.0 0.0004 

3.5 0.0003 

4.0 0.0003 

4.5 0.0002 

5.0 0.0002 

5.5 0.0002 

6.0 0.0002 

6.5 0.0002 

7.0 0.0002 

7.5 0.0001 

8.0 0.0001 

8.5 0.0001 

9.0 0.0000 

For contaminated servings in which E. coli O157:H7 grows (~1%), the predictions in Table TR 

3 are separated into quintiles and the midpoints of each quintile (i.e., 10th, 30th, 50th, 70th, and 

90th percentile) are used to represent the logs of growth (Table TR 4). 

18
Table TR 4. Midpoints of quintiles for logs of growth of E. coli O157:H7 in contaminated 

servings of steaks and roasts during storage and handling (i.e., for G>1, or Log(G)>0). 

Percentile Log(G) 

10th 0.500 

30th 0.578 

50th 0.972 

70th 1.827 

90th 4.819 

3.1.3. 

3.1.4. 

Curve for BPSBC

The number of bacteria per serving before growth (BPSin) is multiplied by the amount of growth 

to determine the bacteria per serving before cooking (BPSBC). 

eqn (21) BPSBC = BPSin x G, 

The result of eqn (21) was shown in Figure TR 3. 

Uncertainty in BPSBC 

We are uncertain about our assumptions concerning the derivation of the BPSBC. Specifically, 

ground beef contamination may not be a reasonable surrogate for steak/roasts, and 

handling/storage of steaks/roasts may differ from handling/storage of ground beef. 

Consequently, we model uncertainty about BPSBC using lower and upper bound distributions. 

To derive these distributions we assume: 

BPSin=0 for 99.998% of servings in the lower bound, 

BPSin=0 for 99.8% of servings in the upper bound, 

the fraction of servings frozen is 80% in the lower bound or 20% in the upper bound, 

different quintile values for BPSin and G (Table TR 5) derived from uncertainty in the 

ground beef model’s predictions. 

Table TR 5. Assumed uncertainty bounds for quintiles of distributions when BPSin>0 and 

Log(G)>0. 

BPSin Log(G) Percentile Lower Upper Lower Upper 

10th 1 1 0.250 0.500 

30th 1 3 0.285 0.620 

50th 1 10 0.479 1.097 

70th 1 32 0.883 2.226 

90th 1 100 2.242 5.637 

19
3.2. 

3.2.1. 

Attenuation factor, AF 

Available data 

Sporing (1999) reports that the process of blade tenderization translocates 3-4% of the surface 

bacteria to the interior. She also provides data on cooking effectiveness for non-tenderized and 

tenderized beef steaks using three cooking methods (skillet frying, grilling, and broiling) and 

three steak thicknesses (1.3 cm, 1.9 cm, and 3.2 cm). Summaries of the results for each of the 

three cooking methods appear in Table TR 6. 

Table TR 6. Summary of log reductions of E. coli O157:H7 for three different cooking methods 

and three different temperatures for non-tenderized and tenderized steak, Sporing (1999) 

Cooking method 

Broiling (average 

of 3 replicates) 

Grilling (average 

of 6 replicates) 

Skillet frying 

(average of 3 

replicates) 

Steak 

Thickness 

(cm) 

Cooking 

Temperature 

(C) Nontend 

Tend Nontend Tend Nontend Tend 

48.9 4.3 2.3 

54.4 2.7 4.4 3.4 2.4 2.6 3.1 

60.0 5.1 5.5 2.6 3.5 1.2 1.8 

65.6 5.9 6.1 4.2 4.5 3.5 2.7 

71.1 6.0 5.9 5.0 5.6 4.0 3.8 

1.3 

76.7 6.4 6.1 5.6 4.8 3.7 5.9 

48.9 5.0 3.9 

54.4 4.7 5.1 3.3 3.5 1.9 0.5 

60.0 5.8 6.5 5.4 4.2 2.0 0.9 

65.6 6.6 6.3 5.6 5.2 2.1 2.3 

71.1 6.6 6.5 5.5 4.5 4.1 4.0 

1.9 

76.7 6.6 6.5 6.2 5.9 5.0 2.8 

48.9 4.9 2.9 

54.4 5.1 6.3 5.1 3.8 2.6 3.1 

60.0 6.3 6.3 6.0 4.9 4.4 1.6 

65.6 6.4 6.3 6.0 5.4 5.0 3.7 

71.1 6.4 6.3 5.9 5.1 3.8 2.9 

3.2 

76.7 6.4 6.3 6.1 5.3 4.0 4.3 

Examination of the results suggest that broiling is more effective at killing E. coli O157:H7 than 

grilling which in turn is more effective than skillet frying. Further, there seems to be a 

suggestion that tenderized steaks are likely to have more E. coli O157:H7 remaining after 

cooking than non-tenderized steaks. 

Figure TR 8, Figure TR 9, and Figure TR 10 display the results in Table TR 6. Separate charts 

are displayed for each thickness of steak and for tenderized and non-tenderized steaks. Within 

each chart results are separated by cooking method. These charts display the results as 

20
attenuation factors rather than log reductions. A log reduction of 2 is equal to an attenuation 

factor of 10-2 or 0.01. This attenuation factor can then be multiplied by a starting number of 

organisms to give the number of expected surviving organisms. A smaller attenuation factor is 

thus associated with a higher log reduction (i.e., more effective cooking). 

Not-tenderized, 1.3 cm thick

1.E-09

1.E-08

1.E-07

1.E-06

1.E-05

1.E-04

1.E-03

1.E-02

1.E-01

1.E+00

45 50 55 60 65 70 75 80

Cooking Temperature (C)

Attenuation factor

Broiling Grilling Frying

Tenderized, 1.3 cm thick

1.E-09

1.E-08

1.E-07

1.E-06

1.E-05

1.E-04

1.E-03

1.E-02

1.E-01

1.E+00

45 50 55 60 65 70 75 80

Cooking Temperature (C)

Attenuation factor

Broiling Grilling Frying

Figure TR 8. Summary of Sporing (1999) data for not-tenderized and tenderized steaks 1.3 cm 

thick. 

Not-tenderized, 1.9 cm thick

1.E-09

1.E-08

1.E-07

1.E-06

1.E-05

1.E-04

1.E-03

1.E-02

1.E-01

1.E+00

45 50 55 60 65 70 75 80

Cooking Temperature (C)

Attenuation factor

Broiling Grilling Frying

Tenderized, 1.9 cm thick

1.E-09

1.E-08

1.E-07

1.E-06

1.E-05

1.E-04

1.E-03

1.E-02

1.E-01

1.E+00

45 50 55 60 65 70 75 80

Cooking Temperature (C)

Attenuation factor

Broiling Grilling Frying

Figure TR 9. Summary of Sporing (1999) data for not-tenderized and tenderized steaks 1.9 cm 

thick. 

21
Not-tenderized, 3.2 cm thick

1.E-09

1.E-08

1.E-07

1.E-06

1.E-05

1.E-04

1.E-03

1.E-02

1.E-01

1.E+00

45 50 55 60 65 70 75 80

Cooking Temperature (C)

Attenuation factor

Broiling Grilling Frying

Tenderized, 3.2 cm thick

1.E-09

1.E-08

1.E-07

1.E-06

1.E-05

1.E-04

1.E-03

1.E-02

1.E-01

1.E+00

45 50 55 60 65 70 75 80

Cooking Temperature (C)

Attenuation factor

Broiling Grilling Frying

Figure TR 10. Summary of Sporing (1999) data for not-tenderized and tenderized steaks 3.2 cm 

thick. 

3.2.2. 

3.2.3. 

 

Interpreting the data: The Plateau Effect 

From the data in Figure TR 8, Figure TR 9, and Figure TR 10, it is clear that at the detailed 

level, there are many not fully controlled variables in this rather difficult experiment. 

Nevertheless, taken as a whole, or “gestalt,” certain broad conclusions can be drawn. We will 

attempt to bring these out, in 3.2.3, in the course of fitting smooth curves to the data. 

First, however, we note in Figure TR 8, Figure TR 9, and Figure TR 10, a flattening out or 

“plateauing” effect at higher cooking temperatures, most notably in the broiling curves. This is 

an artifact of the way the data was plotted and is not to be taken seriously. Indeed, as we shall 

show in 3.2.3. , rather than flattening out, these cooking attenuation curves should approach 

vertical asymptotes, expressing the fact that for each cooking method there is a cooking 

temperature at which all the bacteria are killed1

. 

Fitting the Data to Smooth Curves 

In this section we shall fit smooth curves to the data. The resulting curves will then constitute in 

effect a model of the effect of cooking. Our choice of model, then, is based on two fundamental 

physical/biological assumptions: 

1

 Indeed, the fact that the three cooking methods seem to have different vertical asymptotes suggests the presence 

of another strong uncontrolled variable (perhaps cooking time?) that varies between the cooking methods. 

22
Higher cooking temperatures kill more bacteria. Thus they should result in greater log 

reductions (lower attenuation factors). The empiric evidence in Table TR 6 often seems to 

contradict this assumption. For example broiling not-tenderized beef at 48.9 C resulted in a 4.3 

log reduction while increasing the temperature to 54.4 C resulted in only a 2.7 log reduction. It 

seems implausible to suggest that bacteria that are killed at 48.9 C would survive temperatures 

above that. Thus, such a discrepancy is likely due to variations, errors, or uncontrolled variables 

within the study design or methods. 

There is a temperature at which all bacteria are killed. Bacterial death is often thought of in 

probabilistic terms. In other words, a 2 log reduction results in an attenuation factor of 0.01 

which means that 1% of the starting bacteria are likely to survive. Nevertheless, there must be a 

temperature at which the probability of survival is zero. 

These two assumptions mean that when attenuation factor is plotted against cooking 

temperature, the resultant curve must have an increasingly negative slope as temperature 

increases, approaching a vertical asymptote as shown in Figure TR 11. 

Figure TR 11. Sample attenuation curve 

1.E-09

1.E-08

1.E-07

1.E-06

1.E-05

1.E-04

1.E-03

1.E-02

1.E-01

1.E+00

45.0 55.0 65.0 75.0

Cooking Temperature (C)

Attenuation factor

3.2.4. Modeling the attenuation curve 

A function was developed to model the attenuation curves while satisfying the two modeling 

assumptions. Eqn (23) illustrates this function where AF is the attenuation factor, k is a 

constant, T is the internal temperature, and T0 is the temperature below which no bacteria are 

killed. 

( ( )) 0 1)( TTk Log AF e −× eqn (22) −=

23
For all thicknesses and cooking methods T0 was set at 40 C. We thus assumed that no 

attenuation would occur at a temperature of 40 C (104 F). Different “k”s were chosen for each 

thickness, cooking method, and for tenderized and not-tenderized steaks. Curves were selected 

on a visual judgment of the best fit to the data and discounting the plateau effect. Figure TR 12, 

Figure TR 13, and Figure TR 14 show the fitted curves for each thickness, cooking method, and 

tenderization combination. 

Figure TR 12. Best fit curves to Sporing (1999) data for broiling, grilling, and frying 1.3 cm 

thick not-tenderized and tenderized steaks. 

Not-tenderized, 1.3 cm thick

1.E-09

1.E-08

1.E-07

1.E-06

1.E-05

1.E-04

1.E-03

1.E-02

1.E-01

1.E+00

45 50 55 60 65 70 75 80

Cooking Temperature (C)

Attenuation factor

Broiling Grilling Frying

FitBroiling FitGrilling FitFrying

Tenderized, 1.3 cm thick

1.E-09

1.E-08

1.E-07

1.E-06

1.E-05

1.E-04

1.E-03

1.E-02

1.E-01

1.E+00

45 50 55 60 65 70 75 80

Cooking Temperature (C)

Attenuation factor

Broiling Grilling Frying

FitBroiling FitGrilling FitFrying

Not-tenderized, 3.2 cm thick

1.E-09

1.E-08

1.E-07

1.E-06

1.E-05

1.E-04

1.E-03

1.E-02

1.E-01

1.E+00

45 50 55 60 65 70 75 80

Cooking Temperature (C)

Attenuation factor

Broiling Grilling Frying

FitBroiling FitGrilling FitFrying

Tenderized, 3.2 cm thick

1.E-09

1.E-08

1.E-07

1.E-06

1.E-05

1.E-04

1.E-03

1.E-02

1.E-01

1.E+00

45 50 55 60 65 70 75 80

Cooking Temperature (C)

Attenuation factor

Broiling Grilling Frying

FitBroiling FitGrilling FitFrying

Figure TR 13. Best fit curves to Sporing (1999) data for broiling, grilling, and frying 1.9 cm 

thick not-tenderized and tenderized steaks. 

24
Figure TR 14. Best fit curves to Sporing (1999) data for broiling, grilling, and frying 3.2 cm 

thick not-tenderized and tenderized steaks. 

3.2.5. 

3.2.6. 

Inferences From the Fitted Curves Regarding the Effect of 

Tenderization. 

Not-tenderized, 1.9 cm thick

1.E-09

1.E-08

1.E-07

1.E-06

1.E-05

1.E-04

1.E-03

1.E-02

1.E-01

1.E+00

45 50 55 60 65 70 75 80

Cooking Temperature (C)

Attenuation factor

Broiling Grilling Frying

FitBroiling FitGrilling FitFrying

Tenderized, 1.9 cm thick

1.E-09

1.E-08

1.E-07

1.E-06

1.E-05

1.E-04

1.E-03

1.E-02

1.E-01

1.E+00

45 50 55 60 65 70 75 80

Cooking Temperature (C)

Attenuation factor

Broiling Grilling Frying

FitBroiling FitGrilling FitFrying

The fitted curves make it a bit easier to see the effect of tenderization after discounting the 

plateau effect and the other unwanted variabilities present in the raw data. So what is the 

message of these curves? The curves seem to suggest that tenderization causes a slight 

movement to the right, meaning less attenuation, and that this effect increases with steak 

thickness. This movement is consistent with Sporing’s observation of 3-4% translocation, and 

with what one would expect physically. However we emphasize that this is a suggestion only, 

from the data, and that the variability does not permit a firm conclusion. 

Expressing uncertainty in the cooking effect curves 

The limited data available as well as the large apparent fluctuations in attenuation factors, for 

specific thickness and cooking method combinations, suggests that replicate studies could result 

in quite different results. Consequently, it is important to assign a wide band of uncertainty to 

the most-likely curves shown in Figure TR 12, Figure TR 13, and Figure TR 14. 

The most-likely curves were determined in 3.2.3. by visually fitting curves to the full sets of 

data points. Upper and lower bounds were determined in this Section by visually fitting curves 

to envelop all the data points for each thickness and cooking method combination. This resulted 

in curves that were shifted either to the right (for a lower bound, or least effective [pessimistic] 

cooking curve) or to the left (for an upper bound, or most effective [optimistic] cooking curve) 

25
of the most likely curve for each thickness and method. These uncertainty curves are depicted 

in Figure TR 15, Figure TR 16, and Figure TR 17. 

Figure TR 15. Upper and lower bounds for broiling, grilling, and frying for not-tenderized and 

tenderized steaks 1.3 cm thick. 

Figure TR 16. Upper and lower bounds for broiling, grilling, and frying for not-tenderized and 

tenderized steaks 1.9 cm thick. 

Not-tenderized, 1.3 cm thick

1.E-09

1.E-08

1.E-07

1.E-06

1.E-05

1.E-04

1.E-03

1.E-02

1.E-01

1.E+00

45 50 55 60 65 70 75 80

Cooking Temperature (C)

Attenuation factor

Broiling Grilling Frying

Broiling Grilling Frying

Broiling Grilling Frying

Tenderized, 1.3 cm thick

1.E-09

1.E-08

1.E-07

1.E-06

1.E-05

1.E-04

1.E-03

1.E-02

1.E-01

1.E+00

45 50 55 60 65 70 75 80

Cooking Temperature (C)

Attenuation factor

Broiling Grilling Frying

Broiling Grilling Frying

Broiling Grilling Frying

Not-tenderized, 1.9 cm thick

1.E-09

1.E-08

1.E-07

1.E-06

1.E-05

1.E-04

1.E-03

1.E-02

1.E-01

1.E+00

45 50 55 60 65 70 75 80

Cooking Temperature (C)

Attenuation factor

Broiling Grilling Frying

Broiling Grilling Frying

Broiling Grilling Frying

Tenderized, 1.9 cm thick

1.E-09

1.E-08

1.E-07

1.E-06

1.E-05

1.E-04

1.E-03

1.E-02

1.E-01

1.E+00

45 50 55 60 65 70 75 80

Cooking Temperature (C)

Attenuation factor

Broiling Grilling Frying

Broiling Grilling Frying

Broiling Grilling Frying

26
Figure TR 17. Upper and lower bounds for broiling, grilling, and frying for not-tenderized and 

tenderized steaks 3.2 cm thick. 

3.3. 

3.3.1. 

Cooking temperature, T 

Not-tenderized, 3.2 cm thick

1.E-09

1.E-08

1.E-07

1.E-06

1.E-05

1.E-04

1.E-03

1.E-02

1.E-01

1.E+00

45 50 55 60 65 70 75 80

Cooking Temperature (C)

Attenuation factor

Broiling Grilling Frying

Broiling Grilling Frying

Broiling Grilling Frying

Tenderized, 3.2 cm thick

1.E-09

1.E-08

1.E-07

1.E-06

1.E-05

1.E-04

1.E-03

1.E-02

1.E-01

1.E+00

45 50 55 60 65 70 75 80

Cooking Temperature (C)

Attenuation factor

Broiling Grilling Frying

Broiling Grilling Frying

Broiling Grilling Frying

Available data 

Cooking temperatures for beef are included in the category of beef, pork, and lamb in the FDA 

Home Cooking Temperature Interactive Database (2000). The cumulative frequency of 

temperatures is shown in Figure TR 18. 

0.0

0.2

0.4

0.6

0.8

1.0

35 45 55 65 75 85 95 105

Temperature (C)

Cumulative frequency

Figure TR 18. Internal cooking temperatures for the category of beef, pork, and lamb in the 

FDA Home Cooking Temperature Interactive Database (2000) 

27
The minimum reported internal cooking temperature was 37.8 C. The maximum reported 

temperature was 104.4 C. The mean reported internal cooking temperature was 68.2 C. A 

summary of the data is given in Table TR 7. 

Table TR 7. Summary of the FDA Home Cooking Temperature Interactive Database (2000) for 

beef, pork, and lamb (n=584) 

Temperature range (C) Observations Frequency

37.8 to 51.1 59 0.101 

51.1 to 64.4 158 0.271 

64.4 to 77.8 242 0.414 

77.8 to 91.1 121 0.207 

91.1 to 104.4 4 0.007 

3.3.2. 

3.3.3. 

Adjusting cooking temperature for beef 

The data from the FDA Home Cooking Temperature Interactive Database do not specify 

whether each observation is for beef, pork, or lamb. Nevertheless, it is likely that beef is 

routinely cooked to temperatures lower than for pork. As an example, the FDA Model Food 

code recommends cooking beef and lamb to at least 63 C and pork to at least 68 C. Thus, it is 

likely that the lower temperatures in the database are more representative of beef than the higher 

temperatures. To account for this, the temperatures are shifted down 3 C. 

Accounting for uncertainty in cooking temperature distribution 

Several sources of uncertainty exist for the internal cooking temperature distribution. First, it is 

unknown to what extent beef is represented in these data. As has been noted earlier, the 

distribution has been shifted down to account for a possible bias if the higher temperatures are 

more representative of pork than beef. The amount of such bias is unknown. Second, it is 

unknown how representative these data are of United States cooking practices. Finally, the raw 

data appears to have some reporting bias. These data were originally recorded in degrees 

Fahrenheit. Of the 584 observations, 252 are for temperatures ending in “0”. The direction of 

this reporting bias is unknown. It is possible that some observers rounded up or down while 

recording temperatures. Thus, temperatures may be shifted several degrees from their true 

values. It is also possible that these recording errors essentially cancel each other over the range 

of the distributions and the given percentiles represent the distribution fairly accurately. 

To account for these potential biases and uncertainty, the data values were increased and 

decreased by 6 C for lower and upper bound curves. This represents a difference between the 

upper and lower bounds of 12 C or 21.6 F. Note that because the lower bound is defined as the 

bound that would result in less likelihood of illness, it is represented by higher temperatures 

while the upper bound is represented by lower temperatures. 

28
Figure TR 19 shows the resultant modeled cumulative distributions for internal cooking 

temperatures. Note that the lowest point of the curve for the upper bound, which represents 

20% of all cooking occasions approaches 42 C or 108 F. 

0.0

0.2

0.4

0.6

0.8

1.0

40.0 50.0 60.0 70.0 80.0 90.0

Temperature (C)

Cumulative frequency

Lower Most Likely Upper

Figure TR 19. Cumulative frequency of modeled internal cooking temperatures 

3.4. 

3.4.1. 

Path fractions 

The model simulates three different cooking methods and three different steak thicknesses for a 

total of nine different paths. It is assumed that the fraction of product going into any one path is 

identical for not-tenderized and tenderized steaks and regardless of whether steaks are 

contaminated or if growth occurs. 

Available data 

Sporing (1999) studied three different cooking methods: broiling, grilling and skillet frying. 

Bogen (2001) reports that these three methods account for approximately 56% of ground beef 

servings. Within these three methods skillet frying accounts for approximately 40% of the 

servings while broiling and grilling each account for 30%. 

Sporing (1999) studied steaks of three different methods: 1.3 cm, 1.9 cm, and 3.2 cm. No data 

is available on the proportion of steaks cooked at these three thicknesses. Thus, it is assumed 

that each of these thicknesses accounts for 1/3 of all steak servings. 

29
3.4.2. Modeled path fractions 

Table TR 8 shows the nine different paths modeled for not-tenderized and tenderized steaks and 

the fraction of servings for each path. 

Table TR 8. Path fractions by steak thickness and cooking method 

Steak Cooking Method 

Thickness Broil Grill Fry 

1.3 cm 0.10 0.10 0.13 

1.9 cm 0.10 0.10 0.13 

3.2 cm 0.10 0.10 0.13 

3.4.3. 

3.5. 

3.5.1. 

Uncertainty in path fractions 

Uncertainty exists for both cooking method and steak thickness. Additionally, there are other 

cooking methods and steak thicknesses that have not been modeled. Thus, a great deal of 

uncertainty has been attached to these inputs in order to explore their effect. At the upper bound 

for each of the cooking methods it is assumed that the method accounts for approximately 90% 

of all steaks. At the lower bound it is assumed that the method accounts for only 1% of all 

steaks. The same upper and lower bounds are assumed for steak thicknesses. 

Dose-response curve, DRC 

Human outbreak data 

There are three human outbreaks of E. coli O157:H7 illness from which pertinent data are 

available. These are: 

An outbreak in school children and teachers in Morioka City, Japan, (Shingawa 1997; report is 

in Japanese) was discussed in the Netherlands risk assessment (Nauta 2001). This outbreak 

involved school-aged children and their adult teachers. The implicated food was salad and 

seafood sauce. The estimated percent ill and dose for children and adults was not substantially 

different (Table TR 9). The fraction of infected individuals who became ill was 55%. Infection 

status was determined using fecal culture of exposed individuals. 

An outbreak from contaminated hamburgers in the Pacific northwest U.S. is discussed in Powell 

(2000) and elsewhere. Various estimates of the average dose consumed and the percent ill from 

this outbreak are available. We have used the estimate from FSIS (ECRA 2001) that increases 

the reported percent ill by a factor of 10 to account for unreported cases (Table TR 9). 

Another outbreak occurred in the Chiba Prefecture, Japan. This outbreak involved very young 

children (ages 3-5 years) and was caused by consuming contaminated melons (Uchimura 1997: 

30
as translated and interpreted by Dr. Fumiko Kasuga2

). The average level of contamination in 

the implicated melon was 43 CFU/gram (by MPN method). It was estimated that each child 

consumed about 50 grams of melon. Therefore, the estimated dose ingested for this age group 

is 2150 organisms per serving. The reported percent of young children ill (77%) was larger than 

the infection fraction (54%). Because infection is a necessary precursor to illness, these results 

are counterintuitive. Explanations for this discrepancy include the possibility that some truly 

infected children were test-negative on fecal examine; or the possibility that some illnesses were 

incorrectly attributed to E. coli O157:H7. There were also four adults who were thought to have 

consumed the implicated melon. All four were culture-positive, but none were symptomatic. 

Information about the amount of melon consumed per adult was not provided, so we assume the 

dose is similar to that estimated for the young children. 

Table TR 9. Human outbreak evidence used for development of DRC. 

Outbreak Age 

exposed 

Number 

exposed 

Number 

infected 

Number 

ill 

Percent 

ill 

Dose 

ingested* 

Morioka City, 

Japan 

Adult 43 7 4* 9% 35 

Morioka City, 

Japan 

School 

children 

828 208 114* 14% 31 

Pacific 

northwest, U.S. 

Various 

ages 

52,600* Not 

reported 

3740* 7% 23 

Chiba Prefecture, 

Japan 

Young 

children, 

3-5 yrs. 

old 

44 24 34 77% 2150 

Chiba Prefecture, 

Japan 

Adults 4 4 0 0% 2150 

*Estimated 

3.5.2. 

 

Interpreting the data 

The outbreak evidence provides limited illumination regarding the true dose-response curve, or 

curves, for E. coli O157:H7. 

• Two of the outbreaks involve similarly small doses and, therefore, can only serve to inform 

a small part of the dose-response curve. 

• The one outbreak reporting a larger dose involved very young children. If this age group is 

more susceptible to infection and/or disease than the general population, then the true doseresponse curve for this age group must be distinguished from the general population’s doseresponse curve. Support for the notion of different dose-response curves for young children 

and adults is demonstrated by the failure of any of the presumably exposed adults, in the 

second Japanese outbreak, to become ill. While these four adults might not have consumed 

2

 National Institute of Infectious Diseases, Japan 

31
the same dose as the children, it seems difficult to argue that they consumed much less than 

3-5 year-old children. Yet, this discrepancy in percent ill may be an artifact of an imperfect 

outbreak investigation (e.g., maybe exposed adults were not followed sufficiently over time 

to identify their illness). 

• We are uncertain about the true percent ill and doses occurring in these reported outbreaks. 

For the two low dose outbreaks, the number ill was derived from a series of assumptions. 

For the larger dose outbreak, the percent ill is reportedly greater than the percent infected. 

Obviously, the above outbreak data leaves much to be desired, but it is the best we have. In fact 

it is all we have. So we use it to draw a “best estimate” DRC and then put large uncertainty 

bounds on that curve. 

3.5.3. Fitting the “best estimate” curve to the data

We are interested in describing the functional relationship between dose consumed and fraction 

of illness per serving for the general population. 

Figure TR 20 shows the data points corresponding to the outbreaks. Overlaid on these points 

are the DRCs for ground beef (ECRA 2001), Shigella dysenteriae, and Enteropathogenic E. coli

(EPEC) which are thought to be plausible upper and lower bounds for E. coli O157:H7. 

The most likely curve in Figure TR 20 is not consistent with the outbreak data. Because three of 

the outbreak data-points relate to doses between 20 and 40 bacteria, and these data suggest 

similarity fractions ill, we move the most likely curve to the left to intersect these data points. 

Similarly, the boundary curves are shifted to envelop the outbreak data. 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 1 2 3 4 5 6 7 8 9 10 11

Log (Dose)

Fraction ill

Japan 1 children Japan 1 adults

Japan 2 young children Japan 2 adults

U.S. outbreak ECRA Upper bound

ECRA Most likely ECRA Lower bound

Figure TR 20. Plotted human E. coli O157:H7 outbreak data and the predicted dose-response 

curves from the ground beef risk assessment (ECRA 2001). 

32
Figure TR 21 diagrams the adjusted dose-response curves. The most likely curve intersects the 

cluster of outbreak points at lower doses, but remains well to the right of the outbreak point for 

very young children. We would expect that a dose-response curve for young children (<5 years 

old) would be shifted to the left of this most likely curve to account for that data point. The 

boundary curves envelop the outbreak data and express our uncertainty regarding the true doseresponse curve for the general population. Nevertheless, the upper bound curve may also reflect 

a more likely curve for susceptible sub-populations such as children <5 years old. 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 1 2 3 4 5 6 7 8 9 10 11

Log (Dose)

Fraction ill

Japan 1 children Japan 1 adults

Japan 2 young children Japan 2 adults

U.S. outbreak Upper bound

Most likely Lower bound

Figure TR 21. Dose-response curves used in non-intact beef model to represent the relationship 

between average dose consumed and the fraction of the general population who become ill. The 

upper bound curve, in this case, may be reflective of a dose-response curve for highly 

susceptible individuals. 

4. Results 

The probability of E. coli O157:H7 surviving typical cooking practices in either tenderized or 

not-tenderized steaks is minuscule. As can be seen in Figure TR 22, 0.000026 percent (i.e., 2.6 

of every 10 million servings) of steaks that are not tenderized contain one or more bacteria. For 

tenderized steaks, 0.000037 percent (i.e., 3.7 of every 10 million servings) contain one or more 

bacteria. 

As shown in Figure TR 22, illness seldom occurs at doses less than 10 bacteria per serving. At 

a dose of 100 bacteria, approximately 16 percent of those exposed will become ill. The fraction 

of not tenderized servings with doses of 100 or more bacteria is about 1.4 in 10 million, while 

the fraction of tenderized servings with doses >100 is about 1.5 in 10 million. 

Differences in bacterial dose after cooking attributable to tenderized versus not-tenderized 

steaks are minimal at most. Figure TR 22 shows a barely discernable difference at dose levels 

33
greater than 1 between tenderized and not-tenderized steaks. The expected illnesses per serving 

(IPSEV) for tenderized steaks is 1 illness per 14.2 million servings (7.0 x 10 -8). For nottenderized steaks the IPSEV is 1 illness per 15.9 million servings (6.3 x 10 -8). What this means 

is that there will be seven additional illnesses due to tenderization for every billion steak 

servings(7.0 x 10 -8 - 6.3 x 10 -8). 

0.00000001

0.0000001

0.000001

0.00001

0.0001

0.001

0.01

0.1

1

1

10

100

1000

10000

100000

BPSAC (i.e., Dose)

Fraction of servings exceeding 

dose

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Fraction ill

Not-tenderized Tenderized Dose Response

Figure TR 22. Model output showing predicted bacteria per serving after cooking (Dose) and 

corresponding frequency of illness (Dose Response). 

4.1. Uncertainty in the Analysis

A systematic sensitivity analysis was conducted on the above results to measure the effects on 

the output, IPSEV., of uncertainties in the input. A summary of that sensitivity analysis is 

presented in 

Figure TR 23. Values reflecting upper and lower bounds for each identified input were 

compared one-by-one while using the most likely parameters for all other inputs. This analysis 

shows that IPSEV is most sensitive to the inputs for initial contamination, growth effects, and 

health effects (dose response curve – DRC( ) ). For these inputs, the final results differ by 

almost 3 logs, based on whether the upper or lower bounds of uncertainty were used. The initial 

contamination and growth effects are used to calculate the bacteria per serving before cooking 

(BPSBC). Other input values (internal cooking temperature, cooking effect or attenuation factor, 

and fractions of steaks cooked using different cooking methods or at different thicknesses) were 

found to be substantially less sensitive. Although the effect of cooking was initially 

hypothesized to be an important determinant of human illness, its influence was less than that 

for other inputs. 

34
As to distinguishable differences between tenderized and not-tenderized steaks, the sensitivity 

analysis failed to isolate any. Upper and lower ranges of risk moved in tandem for both 

tenderized and not-tenderized steaks. 

Note that the horizontal lines in Figure TR 23 reflect the risk associated with the combination of 

most likely input values. In this case tenderized steaks show marginally higher risk than nottenderized steaks, but confidence in this conclusion is not high. 

1.E-10

1.E-09

1.E-08

1.E-07

1.E-06

1.E-05

BPSin

G

CT

AF

DRC

Fbroil

Fgrill

Ffry

F1.3 cmF1.9 cmF3.2 cm

Input

Expected health effects per serving

Tenderized Not-tenderized

Figure TR 23. Sensitivity of expected health effects per serving, IPSEV, due to various uncertain 

model parameters. 

4.2. Uncertainty in IPSEV

Given the input settings, the model calculates the expected value of the illnesses per serving 

(IPSEV) distribution (eqn (17) ). Because we are uncertain about model inputs (e.g., BPSin, 

CT, AF), we calculate different IPSEV values for different inputs. We use the discrete 

probability technique to propagate uncertainty in model inputs through the model. In this case, 

we’ve assigned probabilities of 0.1 to both the upper and lower bounds for each uncertain input. 

Consequently, we’ve assigned a probability of 0.8 to each uncertain input’s best guess. The 

results of this calculation, for tenderized and not tenderized steaks, are shown in Figure 1. 

35
0

0.1

0.2

0.3

0.4

0.5

0.6

1.E-09 1.E-08 1.E-07 1.E-06 1.E-05

Expected illness per serving

Probability

Not tenderized Tenderized

Figure TR 24. Probability curves of IPSEV for not tenderized and tenderized steaks. 

This figure shows that about 80% of our confidence lies between IPSEV values of 4.5 x 10-8 and 

1.0 x 10-7. Further, it shows that the probability distribution for tenderized steaks is shifted 

slightly towards higher expected illnesses per serving relative to not tenderized steaks. To 

present this effect in another way we show, in Figure TR 25, the probability curve for the 

difference: IPSEV (tenderized) – IPSEV (npn tenderized). 

This figure shows that we are about 80% confident that the difference in IPSEV between 

tenderized and not tenderized steaks lies between 5.7 x 10-11 and 1.3 x 10-7. Also the figure 

shows a 5% probability that this difference is negative (i.e., that IPSEV for tenderized steaks is 

actually less than IPSEV for not-tenderized). 

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

1.E-12 1.E-10 1.E-08 1.E-06 1.E-04 1.E-02 1.E+00

Difference in IPSEV between tenderized and not tenderized

Probability

0

Figure TR 25. Probability distribution for the difference in IPSEV between tenderized and not 

tenderized steaks. 

36
5. References 

Anderson, M.E; Marshall, R.T.; and Dickson, J.S. 1992. Estimating depths of bacterial 

penetration into post-rigor carcass tissue during washing. J. Food Safety. 12:191-198. 

Bird, R.B.; Stewart, W.E.; and Lightfoot, E.N. 1960. Transport Phenomena. John Wiley & 

Sons, Inc., New York. (Ch. 8-15). 

Bogen, K.T., and Keating, G.A. 2001. U.S. dietary exposure to heterocyclic amines. J 

Exposure Analysis and Environmental Epidemiology 11(3):155-168. 

Calicioglu, M.; Kaspar, C.W.; Buege, D.R.; Luchansky, J.B 2002. Effectiveness for spraying 

with Tween 20 and lactic acid in decontaminating inoculated E. coli O157:H7 and indigenous E. 

coli biotype I on beef. J. Food Protection vol.65, No. 1. 

DeZuniga, K.A.; Anderson, M.E.; Marshall, R.T.; Ianotti, E.G. 1991. A model system for 

studying the penetration of microorganisms into meat. J. Food Protection. 54:256-258. 

Doyle, M.P. 1993. E. coli O157:H7- Meatbourne pathogen of serious consequences. Recip. 

Meat Conf. Proc. 46:115-117. 

Doyle, M.P. and Schoeni, J.L. 1984. Survival and growth characteristics of Escherichia coli

associated with hemorrahagic colitis. Appl. Envir. Microbiol. 48:855-856. 

FDA, Food and Drug Administration. 2000. Audits International/FDA Home Cooking 

Temperature Interactive Database, http://www.foodriskclearinghouse.umd.edu/ColdFusion/Cooking/

George, C.; Unruh, J.A.; Marsden, J.L.; Holmes, M. 1998. Kansas State University. 

Unpublished data. 

Gill, C.O. and Penney, N. 1977. Penetration of bacteria into meat. Appl. Environ. Microbial. 

33:1284-1286. 

Jericho, K.W.F., Kozub G.C., Gannon, V.P.J., and Taylor, C.M. 2000. Microbiological testing 

of raw, boxed beef in the context of hazard analysis critical control point at a high-line-speed 

abattoir. J Food Protection 63(12):1681-1686. 

Johnston, R.W. 1978. Some current aspects of the community nutrition institute’s petition 

concerning mechanical tenderization and packing of meat. Proc. Meat Ind. Res. Conf. 

Johnston, R.W.; Harris, M.E.; Moran, A.B. 1979. Effect of mechanical tenderization on beef 

rounds inoculated with Salmonellae. J. Food Safety 1:201-209. 

Jackson, T.C.; Hardin, M.D.; Acuff, G.R. 1995. Heat resistance of Escherichia coli O157:H7 

associated with a food-bourne disease outbreak J. Food Protection. 58:597-603. 

37
Juneja, V.K.; Snyder, O.P.; Williams, A.C.; and Marmer, B.S. 1997. Thermal destruction of 

Escherichia coli O157:H7 in hamburger. J. Food Protection. 60:1163-1166. 

Kaplan, S., and B. J. Garrick. 1981. On the Quantitative Definition of Risk, Risk Analysis, 

1(1):PLG-0196. 

Kaplan, S., and J. C. Lin. 1987. An Improved Condensation Procedure in Discrete 

Probability Distribution Calculations, Risk Analysis, 7(1):PLG-0451. 

Kotula, A.W.; Chesnut, C.M.; Emswiler, B.S.; and Young, E.P. 1977. Destruction of bacteria 

in beef patties by cooking. J. Anim. Sci. 45:54-58. 

Lentz, C.P. 1961. Thermal conductivity of meats, fats, gelatin gels, and ice. Food Technology

21:1143. 

Lindeman, J. 1999. National Cattleman’s Beef Association. Personal communication. 

Maxcy, R.B. 1981. Surface microenvironment and penetration of bacteria into meat. J. Food 

Protection. 44: 550-552. 

Michigan Department of Community Health/Communicable Disease and Immunization 

Division (August 2000). Report: Foodborne Illness Investigation: E. coli O157. 

Miller, S.G. 1975. Mechanical tenderization of meat in the HRI trade. Proc. 28th Ann. Recip. 

Meat conf of Amer. Meat Sci. Assn.

National Advisory Committee for Microbiological Criteria for Foods (September 25, 2001). 

Meeting Notes. See: Federal Register 66(75) April 18, 2001 Notice. 

Nauta, M.J., E.G. Evers, K. Takumi and A.H. Havelaar. 2001. Risk assessment of Shiga-toxin 

producing E. coli O157:H7 in steak tartare in the Netherlands. RIVM report 257851003. 

Powell, M., E. Ebel, W. Schlosser, M. Walderhaug, and J. Kause. 2000. Dose-response 

envelope for Escherichia coli O157:H7. Quant Microbiol 2(2):141-163. 

Purser, D. 2001. Personal communication. San Antonio, TX, Nov 2001. 

Ross Industries, Inc. 1998. Model TC700M Tenderizer. Instruction manual. Ross Industries, 

Inc. Midland, VA. 

Shinagawa, K. et al. Correspondence and problem for Enterohemorrhagic E. coli O157 

outbreak in Morioka city, Iwate. Bulletin of National Institute of Public Health, Vol. 46, No. 2, 

104-112, 1997. (Japanese publication translated and interpreted by Dr. Fumiko Kasuga). 

Sikes, A. and Maxcy, R.B. 1980 Postmortem invasion of muscle food by proteolytic 

bacterium. J. Food Sci. 45:293-296. 

38
Sporing, Sarah B (1999). E. coli O157:H7 Risk Assessment for Production and Cooking of 

Blade Tenderized Beef Steaks. Kansas Statue University Master of Science thesis. Professor: 

Randall K. Phebus. 

Tilden, J. 2001. Personal communication. Michigan Dept of Health, October 2001 

Tilden Jr., J.; Young, W. McNamara, A; Custer, C.; Boesel, B.; Lambert-fair, J.; Majkowski, J.; 

Vugia, D; Werner, S.B.; Hollingsworth, J.; and Morris, J.G. Jr. 1996. A new route of 

transmission of E. coli: infection from dry fermented salami. Am. J. Public Health. 86: 1142-

1145. 

Uchimura, M. et al. An outbreak of Enterhemorrhagic E. coli O157 in a daycare – Chiba 

Prefecture. Infectious Agents Surveillance Report, Vol. 18, No. 12, 309-310, 1997. (Japanese 

publication translated and interpreted by Dr. Fumiko Kasuga). 

USDA FSIS, United States Department of Agriculture, Food Safety and Inspection Service. 

2001. Draft Risk Assessment of the Public Health Impact of Escherichia coli O157:H7 in 

Ground Beef 

USDA FSIS, Nationwide Federal Plant Raw Ground Beef Microbiological Survey [August 

1993-March 1994], 1996. http://www.fsis.usda.gov/OPHS/baseline/contents.htm. 

Wisconsin Department of Health and Family Services/Wisconsin Division of Public Health 

(October 2000). Report: Investigation of an Outbreak of E. coli O157:H7 Infection at the 

Layton Avenue Sizzler Restaurant, Milwaukee, WI; July-August 2000.

39