Integrated Predictive Models and Sensors in Food Supply ...56469607-d45a-42a4-886b-cab0b… ·...
Transcript of Integrated Predictive Models and Sensors in Food Supply ...56469607-d45a-42a4-886b-cab0b… ·...
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Integrated Predictive Models and Sensors in Food Supply Chains to Enhance Food Safety
Mark Tamplin
Food Safety Centre Tasmania Institute of Agriculture
University of Tasmania
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Outline
• Predictive Microbiology – an overview
• Case studies- industry/government/academic partnerships
• Sensors and databases (ComBase)
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Global Food Drivers
• Chronic illness • Immunodeficiency • Consumer
behaviour difficult to change
Nutrition/Health
• Transformational in biology & nutrition
• Novel processing technologies
• Functional ingredients • Nanotechnology
Science & Technology
• Contaminants • Climate Change • Resource conservation
Environment
• Complex global supply chains • Traceability • Physical contaminants • Microbial contamination • Chemical contaminants • Economic adulterants • Allergens • GMOs • Emerging hazards • Biosecurity • Nano safety
Safety
• 2050, 9 billion population
• Urbanisation • Aging population • Increased ability to pay for
value-added products
Demographics
• Global sourcing • Global sourcing of R&D
Globalization
• Increased scrutiny • National vs
International Standards • New risk management
approaches
Regulatory
• Larger than the biggest food processors
• Buying power • Reduced margins
affect systems downstream
Retailers
• Food safety • Converging trends
• health • convenience • premium • ethics
• Animal welfare
Consumer
Courtesy – Martin Cole
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Microbiological Safety of foods:
Key areas of scientific capacity building
Global best practices for identification &
characterization
Speed, precision and insights are actionable
FOODBORNE PATHOGENS
HEALTH SURVEILLANCE
Identification of critical health issues linked to
foods – e.g. top 5 pathogens causing
most illnesses (metrics - DALYs) for prioritization
DIGITAL / MODELING TOOLS
Risk based design of safe:
• Formulations • Processes
• Supply chain
FOOD HYGIENE
• Households • Serviced foods
• Industry
PREDICTIVE
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Climate Change
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Emerging Hazards
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8
0 200 10000 Betweenness centrality
1998 1999 2000 2001 2002 2003 2004 2005 2007 2007 2008
Food import-export ($-value) fluxes “The highway” József Baranyi (personal Communication)
Ercsey-Ravasz, M., Toroczkai, Z., Lakner, Z., Baranyi, J., 2012. PloS One
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Global sources of food (and contamination)
and Martin Cole
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Ho + ΣI + ΣR ≤ FSO
Food Safety Objectives
Food Safety Modernization Act
L.G.M. Gorris / Food Control 16 (2005) 801–809
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Those at risk for serious foodborne illness:
• persons with chronic disease • very young and elderly • immuno-compromised
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Successful risk management systems rely on knowing how hazards respond to environmental conditions.
……such information reduces uncertainty
A basic tenet of food safety
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A successful risk management system relies on knowing how hazards respond to environmental conditions.
……such information reduces uncertainty
A basic tenet of food safety
But are we using all of the available tools to manage risk?
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Predictive Microbiology
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Represent condensed knowledge, which - describe microbial behavior in different environments - help us better understand and manage the ecology of
foodborne microorganisms
)()(11)(
)(
maxmax tx
xtx
tqtq
dtdx
m
−⋅⋅
+= µ
Predictive models
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Predictive microbiology Assumes microbial behavior is: • reproducible • quantifiable by characterizing environmental factors
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• Identify factors that control microbial viability (e.g. temp, aw, pH, organic acids)
• Assist in defining preventive controls (e.g. critical
limits)
• Help regulatory authorities develop standards, and help companies meet standards
• Minimize microbiological testing
• Inform exposure assessment
Benefits of predictive models
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Predictive models advance food safety risk management systems prescriptive outcome-based
…and just as important
flexible
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How can we be sure that we are producing the most effective models?
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Research problem
Experimental design
Data analysis
Publication
Technical Aspects of Applied Research
Data generation
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Interacting with all end-users of the model (defining the intended outcomes)
Determining the necessary resources
Research
Social Aspects
Communicating with end-users
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• Predictive microbiology brings together persons with diverse but complimentary skills, including microbiologists, mathematicians, engineers, and other disciplines.
• Excellent approach for capacity-building
Other associated benefits
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PRIMARY MODEL PRODUCTION
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Experimental design
Extrinsic factors temperature atmosphere (e.g. packaging gas, humidity)
Intrinsic factors food matrix pH water activity additives (e.g. NaCl, acidulants)
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Growth
1CTO10CHAM_AVG()
0
1
2
3
4
5
6
7
8
9
10
0 50 100 150 200 250
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Kinetic parameters
• Lag phase lag phase duration
• Growth growth rate
• Stationary phase maximum population density 1CTO10CHAM_AVG
()
0
1
2
3
4
5
6
7
8
9
10
0 50 100 150 200 250
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Growth Models
Gompertz
Baranyi
( )[ ]{ }MtBCAtx −−−+= expexp)(log
)()(11)(
)(
maxmax tx
xtx
tqtq
dtdx
m
−⋅⋅
+= µ
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Inactivation Models
kteNN −= 0
10 loglog NNt−
( )21
12
log)(
DDTT −
( ) ( )( ) dtFt zrefTtT∫ −=0
/10
Inactivation kinetics
D-value
Z-value
Process lethality
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y=a*e(-x/b)
0.00
1.00
2.00
3.00
4.00
5.00
0 39 78 117 156 195
Slice no.
L. m
onoc
ytog
enes
cou
nts
(log
CFU
/g)
inoculum 8.41 log CFU/blade,room temp., 10 min
inoculum 5.91 log CFU/blade,room temp., 10 min
inoculum 9.00 log CFU/blade,0 degrees, 10 min
inoculum 8.10 log CFU/blade,10 degrees, 10 min
inoculum 8.07 log CFU/blade,room temp., 2.5h
inoculum on salmon 7.63 logCFU (5.53 log CFU/blade),room temp.
Transfer Models
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Modeling the complexity of microbial interactions
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SECONDARY MODELS
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Change in parameter(s) as a function of environmental change
02468
log bacterial
level
0.85 0.90 0.95 1.006
8
water activity
pH
Effect of pH and Water Activity on Microbial Growth
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Probabilistic models
Growth/No-growth boundaries (e.g. product development)
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Growth/No-Growth
Adapted from Ross
0.94
0.95
0.96
0.97
0.98
0.99
1
0 10 20 30 40 50 60
Temperature (
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Growth/No-Growth
Adapted from Ross
0.94
0.95
0.96
0.97
0.98
0.99
1
0 10 20 30 40 50 60
Temperature (
Less risk
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Growth/No-Growth
Adapted from Ross
0.94
0.95
0.96
0.97
0.98
0.99
1
0 10 20 30 40 50 60
Temperature (
More risk
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Measuring Model Performance (validation)
• Bias factor
Bf=10
• Accuracy factor
Af=10
(∑log(GTpredicted/GTobserved)/n)
(∑ log(GTpredicted/GTobserved) /n)
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TERTIARY MODELS
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GR (log cfu/h)=-0.0146+0.0098T -0.0206L-0.2220D – 0.0013TL-0.0392TD+0.0143LD
+0.0001T2+0.0053L2+2.9529D2
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Examples of common model interfaces
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Case Studies
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Case study #1: Vibrio parahaemolyticus and oyster supply chains
Problem: How can companies reduce uncertainties in supply chains?
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Techniques
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Domestic
104
102
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V. cholerae
V. vulnificus
V. parahaemolyticus
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V. cholerae <1% salt
V. vulnificus 1-2% salt
V. parahaemolyticus 2->3% salt
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V. parahaemolyticus 2->3% salt
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Residuals of predicted versus observed log10 V. parahaemolyticus (Vp) densities in oysters versus salinity based on linear regression of log10 V. parahaemolyticus (Vp) densities against water temperature.
0.001
0.01
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1000
5 10 15 20 25 30 35
Water salinity (ppt)
resi
dual
s of
tem
pera
ture
on
ly re
gres
sion
FDA V. parahaemolyticus Risk Assessment 2005
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0.01
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100000
-5 0 5 10 15 20 25 30 35 Water temperature (° C)
V. p
arah
aem
olyt
icus
den
sity
in
oys
ter (
Vp/g
)
Regression fit of log10 V. parahaemolyticus (Vp) densities in oysters versus water temperature (DePaola et al., 1990). Mean log10 Vp/g or median Vp/g (solid line) and 95% confidence limits (dashed lines).
FDA V. parahaemolyticus Risk Assessment 2005
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1
10
100
1000
10000
0 5 10 15 20 25 30 35 40
Cel
ls p
er g
ram
oys
ter
Temperature (C)
Relationship Between Seawater Surface Temperature and V. parahaemolyticus Densities
in Oysters
TEMPgVp ∗+−= 12.003.1)/log(
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Figure 5: Water temperature Figure 6: V. vulnificus baseline levels
Figure 7: V. vulnificus levels at time of consumption Figure 8: Log mean risk at consumption
FAO/WHO Working Group 5 Risk Management Exercise 2006
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Risk Management
FDA V. parahaemolyticus Risk Assessment 2005
1.E-10
1.E-09
1.E-08
1.E-07
1.E-06
1.E-05
1.E-04
1.E-03
1.E-02
pred
icte
d m
ean
risk
per s
ervi
ng
Winter Spring Summer Fall
Effect of potential mitigations on the distribution mean risk of V. parahaemolyticus illnesses per serving associated with Gulf Coast harvest. No mitigation (•);rapid cooling (◊);treatment resulting in 2-log reduction (∆); treatment resulting in >4.5-log reduction (○).
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V. parahaemolyticus harvest control plan
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Climate Change
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Vibrio species
0.01
0.1
1
10
100
1000
10000
100000
-5 0 5 10 15 20 25 30 35 Water temperature (° C)
V. p
arah
aem
olyt
icus
den
sity
in
oys
ter (
Vp/g
)
• Vibrio diseases are increasing • Outbreaks of V. parahaemolyticus
• Example: 2004-2007- outbreak in Puerto Montt, Chile • >7,000 cases • O3:K6 serotype • El Nino Southern Oscillation (ENSO)
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A Predictive Model to Manage the Risk of Vibrio parahaemolyticus in Australian Pacific Oysters (Crassostrea gigas) Dr. Judith Fernandez-Piquer
Fernandez-Piquer et al., Appl. Environ. Microbiol. 2011
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Model development
• V. parahaemolyticus growth kinetics measured from 4 - 30oC
• Growth (>15oC) and death rates (<15oC) determined
• Models tested (validated) against naturally-occurring Vp
0
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0 50 100 150
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Model validation
• V. parahaemolyticus growth was measured from 4 - 30oC
• Growth (>15oC) and death rates (<15oC) determined
• Models tested (validated) against naturally-occurring Vp
0
1
2
3
4
5
6
0 200 400 6000
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7
0 50 100 150
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Models for V. parahaemolyticus growth and inactivation, and Total Viable Count
√growth rate = 0.0303 x (temperature - 13.37) R2= 0.92
ln inactivation rate = ln 1.81×10-9 + 4131.2 × (1/(T+273.15)) R2= 0.78
√growth rate = 0.0102 x (temperature + 6.71) R2= 0.92
Vp growth
Vp inactivation
TVC growth
Fernandez-Piquer et al., Appl. Environ. Microbiol. 2011
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http://vibrio.foodsafetycentre.com.au/
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4
6
8
10
12
14
16
18
20
22Te
mpe
ratu
re, °
C
-10 0 10 20 30 40 50 60 70Time, hr
Harvest_loc
Storage_farm
transport_truck storage_domestic
Storage_retail
Transport_domestic
Load Unload
from Madigan 2008
Sensitivity Analysis of Oyster Supply Chains
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4
6
8
10
12
14
16
18
20
22Te
mpe
ratu
re, °
C
-10 0 10 20 30 40 50 60 70Time, hr
Harvest_loc
Storage_farm
transport_truck storage_domestic
Storage_retail
Transport_domestic
Load Unload
from Madigan 2008
Sensitivity Analysis of Oyster Supply Chains
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~$23,000 ~$1.6 million
Refrigeration vs Spoilage Cost Scenarios
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~$23,000 ~$1.6 million
Refrigeration vs Spoilage Cost Scenarios
By investing The industry could save!
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Integrating Sensors and Predictive Models
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refrigerated storage
country import wholesale storage
retail storage
consumer
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Log Vp/g=-2.05+ 0.097*tempwater+0.2*sal-0.0055*SAL2 √growth rate = 0.0303 x (temp-13.37)
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Smart-Trace – a field trial
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Currently, predictive models are not commonly used in real-time (or even retrospectively), due to lack of data capture. Sensors are a solution.
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Smart-Trace tag
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Integration of Time Temperature Indicator (TTI) sensors with predictive models for consumer-direct delivery of food products
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Boxed trim destined for export
Case study #2: Pathogenic E. coli in beef
Problem: What innovations can help export companies more quickly reach their markets?
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Refrigeration Index (RI)
• Previous regulation required carcases to be cooled to 7°C in < 24 hours
– this could be done in many ways with quite different food safety outcomes
• The industry wanted to package hot-boned beef trim
• A predictive model was developed through a government-industry-university partnership
• The Refrigeration Index predicts potential growth of E. coli based on a growth model
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RI now part of Australian food safety law for meat
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total predicted E. coli growth during chilling;
determines whether product is “acceptable” (<
1 log potential growth)
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Benefits
Analysis by Australian Centre for International Economics
• $160 million increase in Australia’s GDP
• $280 million in social benefits
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Case study #3: Clostridium perfringens in cooked primals
Problem: How can companies better manage temperature deviations when cooling primals?
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Perfringens Predictor
Previous regulation about cooling cooked primals was highly prescriptive
Occasionally, cooling profiles deviated
Sampling plans and testing were not cost-effective
An outcome-based model was developed through a government-industry partnership
Accepted criteria was <1 log growth of C. perfringens
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Perfringens Predictor
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ComBase (www.combase.cc)
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Vision: ComBase data and models will be used to support global food safety and quality programs. Goal: To engage with the international food microbiology community, and provide it with robust data and models that describe how food safety and spoilage organisms respond to food environments.
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USDA-ARS IFR
UTAS
Hokkaido University Unilever
University of Querétaro
Agricultural University of Athens ●
●
● ● ●
●
●
Rutgers University ● IZSLER ●
ComBase Partners and Associates
?
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ComBase Advisory Group
• Unilever • Nestlé • IZLER • USFDA • USDA • Rutgers University
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The ComBase Scientific Group is being formed, and we are looking for
more interested partners.
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ComBase Browser
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ComBase Browser
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ComBase Browser
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ComBase Browser
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ComBase Predictor
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ComBase Predictor
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• Growth/thermal and non-thermal inactivation
• Shelf-life
• Hazard identification
• Product development
• Process deviations
Applications
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Database
Sensors
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• Predictive microbiology training
• Research collaborations
• ComBase workshops
• Scientist-Student exchange/degrees
Collaborative opportunities
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Thank you for your generous hospitality!