Control of Pathogens in the Food Industry: A Global Food
Company’s Perspective
Controle de Patógenos na Industria de Alimentos: A
Perspectiva de una Empresa Multinacional de Alimentos
III Simpósio Internacional de Inocuidade de Alimentos
(ABRAPA)
VIII Simpósio Brasileiro de Microbiologia de Alimentos
(SBM)
Dr. Paul A. Hall
October 26, 2004
Sao Paulo, Brasil
Sr. Director
Microbiology and Food Safety
Glenview, IL
Kraft Foods – Company Facts
• 2003 net revenues of more than $31 billion.
• Largest food and beverage company in North
America and second largest in the world.
• Brands marketed in over 150 countries globally.
• More than 100,000 employees operating in more
than 68 countries.
• 197 manufacturing facilities worldwide at the end of
2003.
Kraft Foods – Company Facts
– The Kraft brand portfolio is one of the
strongest in the world.
• Number one share position in 11 global
categories, 22 of the top 25 categories in the
U.S., and 18 of the top 25 categories
internationally.
Producing Safe Food is our First
Priority
• Consumer Protection & Trust
– Consumer trust
– Food Safety is critical to that trust
• Business Survival
– Our brands are most important assets
• Industry Responsibility
– Committed to food safety across the
food chain
Methods to Reduce the Risk from
Pathogens in Food*
• Prevent inadvertent contamination
• Inhibit growth
• Remove contamination
* Adapted from Sofos, et al., 1998
Top Line Summary
Public health is best protected by control of
Pathogens via:
• Aggressive environmental monitoring
• Effective corrective actions
• Proper equipment design
• Adherence to GMPs and SSOPs
• Proper handling practice
– Refrigerate perishable RTE products at <40 F
(<4.40º C)
– Consume perishable RTE products quickly
• Appropriate intervention strategies
– Formulation (e.g. lactate salts/sodium diacetate)
– Post-packaging treatments
Pathogen Control Approaches/
Interventions
• HACCP and Prerequisite Programs
• Sanitation and GMP’s
– Environmental Monitoring Program
• Ingredient Specifications
• Product Formulation
• Vendor Qualification & Quality Expectations
• Auditing and Certification Programs
• New Processing Technologies
• Improved Detection Methods
• Good agricultural Practices/On-Farm Controls
Pathogen Control - Listeria
monocytogenes as an Example
• Certain foods pose an increased risk of being
associated with listeriosis
• These foods have the following properties:
–
–
–
–
–
Have the potential for contamination with LM
Support the growth of LM to high numbers
Are ready-to-eat foods
Require refrigeration
Stored for extended periods of time
Pathogen Control - Listeria
monocytogenes as an Example
• Foods can be classified according to their
risk based on their properties and history
of known illness
US FDA Listeria Risk Assessment
Decreased Risk per Annum
Clusters A and B
Clusters C and D
Cluster E
Very High Risk
High Risk
Moderate Risk
Deli Meats
Pâté and Meat Spreads
Frankfurters (not reheated)
Unpasteurized Fluid Milk
No food categories
Cluster
1
Smoked Seafood
High Risk
High Fat and Other Dairy Products
Moderate Risk
Cooked RTE Crustaceans
Moderate Risk
No food categories
Cluster
2
Pasteurized Fluid Milk
Soft Unripened Cheese
Moderate Risk
No food categories
Moderate Risk
Low Risk
Deli-type Salads
Preserved Fish
Dry/Semi-dry Fermented Sausages
Raw Seafood
Cluster
3
Frankfurters (reheated)
Fresh Soft Cheese
Fruits
Semi-soft Cheese
Soft Ripened Cheese
Vegetables
Moderate Risk
No food categories
Low Risk
No food categories
Very Low Risk
Cultured Milk Products
Hard Cheese
Ice Cream and Other Frozen
Dairy Products
Processed Cheese
Cluster
4
Differentiating Risk in Processed Meats
• Reheated versus unheated hot dogs
• Dried and semi-dried meats
• Pate
• A significant portion (>70%, hot dogs
and>50 % deli meats) of RTE processed
meats have been formulated with growth
inhibitors
• Deli meats really are four product
categories
– With and without inhibitors
– In store sliced and packaged
– Commercially prepackaged
Industry actions to reduce the risk L.
monocytogenes in RTE products
• Training of industry through comprehensive Listeria
control workshops.
• Review of Listeria control workshop materials with
USDA staff
• The use of a thermal treatment after a product has
been packaged to destroy Listeria monocytogenes.
• Use of new ingredients to inhibit the growth of
Listeria monocytogenes on ready-to-eat meat and
poultry. Many products now contain these
ingredients.
• Development of new principles for processing
equipment design that facilitate sanitation and reduce
the possibility of bacteria being "harbored" in tiny
spaces like the thread of an exposed screw or a
hollow roller on a conveyer belt.
Industry actions to reduce the risk L.
monocytogenes in RTE products
• Sophisticated new environmental sampling
programs that work to target Listeria in the plant
environment so it can be destroyed before it is
transferred to products.
• Research to discover new technologies.
• Declaration by the meat and poultry industry that
food safety is a "non-competitive issue," which
resulted in the free exchange of food safety
information among competitors.
Prevalence of Listeria monocytogenes* in
Sliced Lunchmeats and Franks
Sliced Lunchmeats
Franks
Percent Positive
10
8
6
4
2
0
1996
1997
1998
1999
2000
2001
2002
Year
*
FSIS Results of ready-to-eat products analyzed for Listeria monocytogenes
Incidence per 100,000 Population
Incidence of Foodborne Illness
1996-2002: Listeria*
0.6
National
Health
Objective:
.25
0.5
0.4
0.3
0.2
0.1
0
1996
1997
1998
1999
2000
2001
2002
*Preliminary FoodNet Data on the Incidence of Foodborne Illnesses --- Selected Sites, United
States, 2002
Pathogen Control - Listeria
monocytogenes as an Example
• Product reformulation can be a powerful
tool for reducing consumer risk
• Microbial models can be used to optimize
product quality and product safety
Modeling Approaches
•
Kinetic Models
1. Fit growth curves, derive rate constants.
2. Develop multiple regression model for growth rate
constants as a function of predictor variables.
3. Predict amount of growth after time.
•
Boundary model:
1. Define growth threshold  measure time to growth.
2. Develop generalized regression model for time to
growth as a function of predictor variables.
3. Predict time before growth occurs.
Intro to Boundary Models
• Predict time-to-event (e.g., failure, spoilage, growth) as a
function of “predictor” variables.
• Commonly used in:
— Engineering: time-to-failure of a new design
— Medicine: efficacy of different drugs and doses on mortality
— Social sciences: prisoner recidivism by treatment program
• Use generalized regression to get predictive model and
develop contour maps to show boundary between
“growth” and “no-growth”.
— Handles censored observations.
— Uses maximum likelihood estimation (get log likelihood, not R2.)
Define Growth Threshold
An increase of 1 log10 or more in L. monocytogenes
count, determined by expert review of growth
curves:
• Smallest change distinguishable from “noise”.
• IFT expert panel 2001 …“a 1 log increase [is] an
appropriate level of control for L. monocytogenes”.
– Evaluation and definition of potentially hazardous foods. December 31,
2001. IFT/FDA contract no. 223-98-2333 task order no.4. Chapter 6
section 9 pass/fail criteria.
http://www.foodprotect.org/pdf/hazard_foods/chapter6.pdf
Experimental Design – Processed
Meats
Central composite design for four continuous variables:
NaCl % :
Moisture %:
0.8
1.5
2.2
2.9
3.6
45.5 55.0 64.5 74.0 83.5
Na diacetate %:
0.0
0.05 0.10 0.15 0.20
K lactate syrup %:
0.25 2.5
4.75 7.0
9.25
• Repeated for uncured products,  5th variable (cured/uncured).
• Model products were made, inoculated, stored at 4 °C, and assessed
every 2 weeks for LM count.
• Seman, D.L., et al. (2002) J. Food Protection, 65, 651-658.
Model Performance: Summary
Observed weeks to 1 log10 growth
Model gives good description of the data used to create it.
100
10
Censored
observations
1
1
10
Predicted weeks to 1 log10 growth
100
Contours of weeks to 1 log growth of L. monocytogenes in
cured products calculated using the boundary model with
growth and no-growth modeling and validation observations
45.5
Lactate %
10
10
Key
8
Shaded graphs
show model design
space
18 weeks
6
4
24 weeks
2
30 weeks
64.5
36 weeks
42 weeks
8
48 weeks
6
4
Model: growth
2
Model: no growth
0
10
Validation: growth
Lactate %
55.0
Lactate %
0
10
10
Validation: no growth
8
≥ 1 but < 2 logs of
growth
6
4
N.B. Positions of validation
points are approximate
2
74.0
Lactate %
0
10
0
8
0.05
Growth and No-growth
space
0.1
Inhibitory pressure on
growth increases from left
to right and from bottom
to top on both the outside
and individual x and y
axes.
6
4
2
83.5
Lactate %
0
10
Hence, “growth space” is
always below and to the
left of the contours. “No
growth space” is always to
above and to the right of
the contours.
8
6
The primary concern is to
avoid growth points in “no
growth” space.
4
2
0
0
0.05
0.1
0.15
Diacetate %
0.8
0.2 0
0.05
0.1
0.15
Diacetate %
1.5
0.2 0
0.05
0.1
0.15 0.2 0
Diacetate %
2.2
Salt %
0.05
0.1
0.15 0.2 0
Diacetate %
2.9
0.05
0.1
0.15
0.2
Diacetate %
3.6
Potential Graphical Output from Boundary Model
Base and modified formulas relative to boundary for 1 log of growth (If
base formula is not shown, salt and/or moisture have been changed)
Product is:
Test 4
8.0
No-growth region
Lactate syrup %
7.0
Cured ?
=
yes
Salt
= 2.50%
6.0
Moisture = 75.0%
5.0
67.5 days
(target - 10 %)
4.0
75 days
(target)
3.0
82.5 days
2.0
(target + 10 %)
1.0
No lactate/
diacetate
Growth region
0.0
0
0.05
0.1
Diacetate %
0.15
0.2
With
lactate/
diacetate
Application
• Simple spreadsheet
– Calculate time to growth from formula
– Calculate lactate from shelf-life
– Plot growth boundary
• Available from Purac America on free
CD
Listeria Growth Inhibition
Estimated Benefit to Public Health*
PROJECT
ZERO
Predicted Log Counts/gm
8.00
1/7,500 risk
7.00
6.00
5.00
1/75 MM risk
4.00
3.00
1/750 MM risk
2.00
1.00
*Based on Growth Model and median mortality risk
for neonates published in FDA/USDA risk analysis Figure IV-5
Estimated 95th Percentile Mortality Risk
- 50 g serving of product
- Lm growth from an initial level of 1CFU/g
Initial
1 CFU/g
After 3 log
Growth
After 6 log
Growth
After 8 log
Growth
Intermediate- Neonatal
Age
5 x 10-12
1 x 10-9
Elderly
2 x 10-9
5 x 10-7
2 x 10-8
1 x 10-6
3 x 10-4
1 x 10-5
8 x 10-5
2 x 10-2
9 x 10-4
4 x 10-11
Source: Interpolation from FDA Fall 2003 Listeria monocytogenes Risk Table IV-12
PROJECT
FORWARD
• Project Forward
controls Listeria in
the environment
• Using environmental
sampling we
systematically seek
out and find sources
and take corrective
action
PROJECT
ZERO
• Goal - Identify possible
technology solutions to
achieve zero pathogen
risk in RTE meat
products
• Through formulation, we
can further reduce risk
resulting in greater public
health protection
Concurrent Approach to Address
Public Health
PROJECT
FORWARD
PROJECT
ZERO
Preventative & Corrective
Actions
Potential Technical
Solutions
• Internal Plants
• Formulation
• External Network
• Product/Process Handling
• Post Packaging
Pasteurization
Project Forward - Listeria
Control Program
PROJECT
FORWARD
3-Stage Approach to Address Preventative & Corrective Actions
Sanitation /
Environmental
Practices
• Intensive
Environmental
swabbing
• Footwear /
clothing
• Traffic patterns
• Sanitation
• Maintenance
Facility /
Equipment Design
• Facility layout
• Floors
• Design for
Sanitation
Personnel
Training
•
•
•
•
GMPs
Maintenance
Sanitation
Behavior based
food safety
Logic Behind Environmental
Control Program
PROJECT
FORWARD
• Listeria Control Equation based on premise
that intensive environmental monitoring is
effective in understanding the plant
environment to control Listeria
Listeria Equation
Traffic
Patterns +
PROJECT
FORWARD
Dry,
Uncracked, Sanitary
Sanitation
GMPs + Clean +
Design + Procedures
Floors
= Listeria Control
Mismanagement of any of the components
may increase the risk of cross
contamination.
Logic Behind Environmental
Control Program
PROJECT
FORWARD
• Listeria Control Equation based on premise
that intensive environmental monitoring is
effective in understanding the plant
environment to control Listeria
• Systematic, disciplined approach to seek out,
find and eliminate the undesirable conditions
which could support harborage or transference
of indicator organisms
PROJECT
FORWARD
Sanitary Zones
Zone 2
Zone 1
Product contact surfaces:
e.g. slicers; conveyors;
peelers; strip tables;
utensils; racks; work
tables; employee hands
Exterior of
equipment;
chill units;
framework;
equipment
housing
Zone 3
Phones;
hand
trucks;
forklifts;
walls;
floors;
drains
Zone 4
Locker rooms;
cafeteria;
halls
Environmental Monitoring
Approach
PROJECT
FORWARD
• Timely assessment of control of RTE environment
• Biased intensive sampling during production to
validate all components
• Large surface areas sampled for Listeria genus
• Sampling is randomized (by the day of the week
and shift)
• Every RTE processing line must be sampled
weekly
• Sampling plans need to be flexible and tailored
to each specific line and facility
Logic Behind Environmental
Control Program
PROJECT
FORWARD
• Listeria Control Equation is based on premise
that environmental monitoring is effective in
understanding the plant environment to control
Listeria
• Systematic, disciplined approach to seek out,
find and eliminate the undesirable conditions
which could support harborage or transference
of indicator organisms
• Focus improvement efforts (capital and
resources) as directed by results—
”follow the data”
Logic Behind Environmental
Control Program
PROJECT
FORWARD
Finished product testing has significant
limitations.
Probability of Missing Contamination
Number of
Samples Tested
% Contamination in Lot
10%
2%
1%
0.5%
3
73%
94%
97%
99%
10
35%
82%
90%
95%
60
<0.5%
30%
55%
74%
120
<0.5%
8.5%
30%
55%
180
<0.5%
2.6%
16%
41%
240
<0.5%
0.8%
9%
30%
Logic Behind Environmental
Control Program
PROJECT
FORWARD
• Statistics demonstrate that finished product
testing has severe limitations
– Finished product sampling is not preventative and
does not help identify root cause of contamination
• Disciplined approach to monitoring promotes
knowledge and awareness of the
environmental conditions that could result in
product contamination
– If there is an effective kill step in the process, and if
there is no Listeria in the environment, there will be
no Listeria in the finished product
• Public health protection is better served with
an aggressive environmental program
Logic Behind Environmental
Control Program
PROJECT
FORWARD
• To verify effectiveness of the program, we
monitor all components in the Listeria equation
• Of ~100 RTE meat production lines
– 50% no positive contact surfaces
– 84% single occurrence
• These results indicate the level of Listeria is
very low in our environment
• Low levels in the environment are not likely
to result in product contamination
Low Levels in the Environment
Enumeration Data
PROJECT
FORWARD
• Counts of >10 per area swabbed only seen on
floor after 2 shifts, or in niches
• Environmental samples of product contact
surfaces tested for Listeria have been
enumerated. Positive samples that were
enumerated contained less than the detection
limit of the methods (MOX and MPN)
• Data supports concept that random positive
product contact surfaces contain few Listeria
(<10) that can be transferred to product
Corrective Actions
PROJECT
FORWARD
In the event of a positive Listeria species
environmental sample, Kraft requires follow
up/corrective actions. Typical corrective
actions include:
• Review of cleaning records
• Review of environmental data of the
area as well as adjacent areas
Corrective Actions (cont’d)
PROJECT
FORWARD
• Review of line records, for mechanical
downtime
• Audit and interview employees concerning
practices during sanitation, set-up, and
production
• Inspections of the area and equipment for
potential harborage points
• Complete a targeted clean
Benefits of Aggressive Environmental
Monitoring / Corrective Actions
Percent Positive
1.8%
1.48%
1.6%
1.4%
1.2%
1.0%
0.8%
0.6%
0.4%
0.2%
0.0%
1999
PROJECT
FORWARD
Zone 1 Positive
Percent Listeria spp. Positive Annual
1.05%
0.54%
0.23%
0.16%
2000
2001
2002
2003
Year
Graph 1 values calculated with the formula
(total zone 1 composite + total follow up
positive) / (total zone 1 composite samples * 5)
+ (total follow
up samples)
Results —
Reduced Zone 1 +’s
85% since ‘99
Project Forward
Validation Program
PROJECT
FORWARD
• To measure monitoring program effectiveness,
a validation program is in place to assure that
the samples taken represent the actual
conditions of the entire environment at a given
time.
• Includes multiple sampling points during:
– Pre-op
– Operation
– 2nd shift operation
• One day for two consecutive weeks
• Completed once every six months
Regulatory Goal
• Protect public health
• Success depends upon locating Listeria-finding positive results--and taking proper
action
• Even with effective control, environment
will not be completely Listeria negative
• Utilize appropriate interventions to reduce
public health risk
Summary
Public Health is best protected by:
• Implementation of a validated Listeria
control program
– Aggressive environmental monitoring
– Effective corrective actions
– Incorporation of appropriate intervention
technologies
• Proper handling practices
• No Listeria monocytogenes exceeding
regulatory limit in food in commerce
Obrigado pela atenção!
Perguntas?