Dr. Lihan Huang - Center for Food Safety Engineering - Purdue ...

ERRC Predictive Microbiology
Toolbox and Food Safety
Information Gateway
Lihan Huang, Ph.D
Research Leader
Residue Chemistry and Predictive Microbiology Research Unit
Agricultural
Research
Service

Overview
• Research Unit
• Predictive Microbiology
• Future research and USDA Predictive
Microbiology Tool Box and Food Safety
Information Gateway

Who we are and what we do
• This unit is a brand new unit and was
formed after reorganization in May, 2010.
• I was called to duty on August 1, 2010.
• We have chemists, microbiologists, food
technologists, and engineers.
• Hopefully, we can have a computational
biologist in the future.

The Great Unit of RCPM
• We are a unique group of scientists with diverse expertise
and combined talents to address some of the most
pressing challenges facing the nation’s food supply.
• Together, we strive to conduct both basic and applied
research in science, developing new technologies and
methods to detect chemical residues, predict the fate of
foodborne pathogens, and control their presence in foods.

Four Research Groups
Residue Chemistry
Predictive Microbiology
for Food Safety
Integrated Approach to Process
and Package Technologies
Data Acquisition and Modeling
for Poultry Food Safety

Residue Chemistry Group
Dr. Steven Lehotay, Investigation in all
aspects of chemical residue analysis.
Dr. Marilyn Schneider
Development of methods for
analysis of veterinary drug
residues in food
Vacant (100 applicants)
Expert in Chromatography and
Mass Spectrometry
Dr. Guoying Chen
Application of optical
spectroscopy and other
techniques to rapid food
analysis
Dr. Marjorie B. Medina
Development of immunoassay,
biosensor, immunoaffinity, and other
techniques for detection of
antioxidants, phytoestrogens, endocrine
disruptors, and pathogen toxins

Predictive Microbiology
Dr. Vijay Juneja
Dr. Andy Hwang
Predictive Microbiology Information Portal
Regulations Models Useful Links
• Final Rule on Listeria
monocytogenes in RTE Meat
and Poultry Products
• “Zero Tolerance” Policy
Dr. Lihan Huang

Integrated Approach to Food Safety
Dr. Xuetong Fan
Irradiation and produce safety,
chemical, nutritional and quality
changes
Dr. Tony Jin
Biopolymers,
antimicrobials, and
food packaging
Dr. Sudarsan Mukhopadhyay
Process engineering for
food safety

Dr. Thomas Oscar,
Predictive modeling, risk
analysis and poultry safety
Data Acquisition and Modeling
for Poultry Food Safety

Mathematical Methods and Computer Simulation
in Predictive Microbiology
A fundamental question - how bacteria grow in the eyes of microbiologists
Cytokinesis
Chromosome segregation
Binary fusion
Cell
DNA replication
Chromosome

Binary Fusion

This is an exponential growth process

Modeling Binary Fusion
x(t) = x 0 •2 t
Ln(X) or log 10
(X)
time

The whole world is filled with bacteria!()
If the exponential growth is allowed to proceed
unlimitedly, the whole world may have been
occupied by microorganisms.
Apparently, the nature does not allow unlimited
growth of microorganisms.

The Reality of Bacterial Growth in Food
A three-phase process, progressing from lag
phase, through exponential phase, to and
stationary phase.
Stationary phase
Log concentration
Lag phase
Exponential phase
time

The Easiest Method
• The “Ruler Method” - used in the “stone age” and in classroom teaching
• Makes great sense in the eyes of microbiologists
Log concentration
time

The Manual Method
• Labor-intensive, slow
• Not suitable for large amount of data
• Hard to find a journal to publish your work

Mathematical Modeling
The question is -
Can we use some kind of mathematical
models to describe the bacterial growth
The answer is Yes!

Empirical Models
• Find an equation that resembles growth
curves
• The S-shaped curves
• This approach is primarily used by U.S.
scientists (ARS ERRC) and is the buildingblock
for the early versions of the USDA
Pathogen Modeling Program (PMP)

Empirical Models
• Modified Gompertz model
• Modified logistic model
( t) = L + ( L L ) S( t)
L
0 max
!
0
S
( t)
$ exp
!
= #
!
!"
1+
exp
{%
exp[ % µ ( t % M )]
}
1
[%
µ ( t % M )]
Gompertz
Logistic

Kinetic parameters derived from
empirical models
( t) = L + ( L L ) S( t)
L
0 max
!
0
& =
$
!
M
!
#
!
! M
"
%
%
1
µ
2
µ
Gompertz
Logistics
S
( t)
=
$ exp
!
#
!
!"
1+
exp
{%
exp[ % µ ( t % M )]
}
1
[%
µ ( t % M )]
Gompertz
Logistic
K
=
$ L
!
!
#
! L
!
"
max
max
% L
e
% L
4
0
0
µ
µ
Gompertz
Logistic
Drawback
- These are EMPIRICAL models
- Curve-fitting
- Do not tell too much about the nature of bacterial growth

Mechanistic models
The Baranyi Model It was originally based on Michaelis-Menten kinetics
dq
dt
dC
dt
= ( q
=
µ
q &
q
$ '
1+
%
max
1
C
C
y
max
#
! C
"
Formation of critical substances
Logistic equation
max 0 0
( t) = y + ( )'
$ +
! 0
µ
max
A t ln 1
ymax
' y0
% e "
&
e
µ
t'
h
' e
h
#
1 !"
t ! h
( ) ln( )
0 !"
t!
h
= t + e + e ! e
0
A t
"
h
ln ( q %
&
0
1
# !
' q + $
0
0
= " = " ln
( )
0

Primary Models
• These models have been used for many
years and appeared in numerous
publications
• Both models are widely used in the U.S.-
based publications
• The Baranyi model is predominantly used
outside the U.S.

Progress from ERRC - A New
Primary Model
• Strictly based on the biological phenomenon that
are observed in the experiments
• Basic definition of lag phase – no change in cell
counts
• Exponential growth – log-linear growth curve
• Stationary phase – no change in cell counts

A New Primary Model from ERRC
dC
dt
) C & 1
= µ C
' 1 #
$
( Cmax
% 1+
exp t
[#
"( # !)]
From lag phase to exponential phase
λ, lag phase

Analytical Solution to the New
Growth Model
y
( t) = y0
+ ymax
# ln{ exp( y0) + [ exp( ymax) # exp( y0)
] exp[ # µ $ exp( ymax) B( t)
]}
,
1 1+
exp
( )
(#
!( t # "
)
B t = t + ln
! 1+
exp( !")
where
10
9
8
log(CFU/g)
7
6
5
4
3
2
0 5 10 15 20 25 30 35 40
t (h)
! = 0.1 ! = 0.5 ! = 1 ! = 25

L. Monocytogenes in BHI Broth (37 o C)
10
log(CFU/ml)
9
8
7
6
5
4
3
2
1
0
Lag phase determined by the new model
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
t (h)
G1 G2 G3 G4 Gompertz Baranyi New model

Special case (no stationary phase)
y
( t)
=
y
0
+
&
µ % t
$
1 1+
exp
+ ln
( 1+
exp
['
(
t ' )
]
(()) ! "#
10
9
8
log(CFU/g)
7
6
5
4
3
2
0 10 20 30 40 50 60 70
t (h)
15°C 30°C 37°C

E. coli O157:H7 in beef
20
Rif r , 25°C
20
Wild, 25°C
18
18
16
16
14
14
12
12
10
10
8
8
Ln cfu/g
6
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32
6
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32
20
Rif r , 37°C
20
Wild, 37°C
18
18
16
14
12
10
8
6
0 2 4 6 8 10 12 14 16 18 20 22 24 26
t (h)
16
14
12
10
8
6
0 2 4 6 8 10 12 14 16 18 20 22 24 26
t (h)
Red: Huang model
Green: Baranyi
Yellow: Gompertz

The New ERRC Model
• Directly derived from the fundamental
assumptions of bacterial growth
• All three phases clearly identifiable
• The lag phase and exponential growth
explicitly determined
• Accurate

Progress from ERRC – A More Realistic
Secondary Model
Effect of Temperature on Growth Rate
Optimum Temp
Growth Rate
Min Temp
Max Temp
Temperature

Ratkowsky Models
µ
µ
µ =
=
=
a
a
a
( T ! T )
[ ]
[ ]
1!
e
b T ! T
( T ! T ) 1!
e
b( T ! T ) max
( )
2
T ! T
( ) max
min
min
min

Ratkowsky models - Limitations
Optimum Temp
Growth Rate
Ratkowsky model
Min Temp
Real Max Temp
Real Min Temp
Temperature

Progress from ERRC - A More Realistic
Secondary Model
µ =
a
[ ]
( T ! T )"
1!
e
b( T ! T ) max
min

A New Secondary Model from ERRC
L. Monocytogenes in beef hot dogs

What’s next
Our Short Term Goal -
• Develop a software tool
• Primary models
• Secondary models
• PMP – USDA Pathogen Modeling Program
• PMIP – USDA Pathogen Modeling
Information Portal

Our Long Term Goal - ERRC Predictive
Microbiology Toolbox and Food Safety
Information Gateway
Computation/
Numerical
Analysis
Engine
Data
Processing
Engine
Database
Engine
Mathematical
Model Engine
Menu Bar
Food
Engineering
Engine
Front-End Engine
Documentation
Engine
Report
Engine
System
Update
Engine
Risk Analysis Engine