predictive models to manage food stability in high-value...
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Predictive models to manage food stability in high-value Tasmanian supply chains
Mark TamplinCentre Leader, Food Safety CentreTasmanian Institute of AgricultureUniversity of Tasmania
Long transport distance = innovation
• Important brand
• Eco-tourism
• Food tourism
• Market niche
TIAR‘Clean Green’
Outline
• Pathways to Market research project
• Predictive tools to manage food stability (safety & quality) in:
o beef (vacuum-packaged)
o salmon (Atlantic)
o oysters (Pacific)
Transforming Food Industry Futures Through Improved Provenance Sensing and Choice
Funded by - Australian Research Council, industry investment
To transform Tasmanian value chains, through the a holistic use of ‘intelligent’ information that powers food industry competitiveness, environmental sustainability, and innovation, from producer to consumer
Project goal
External Data
Sources
MLA:NLIS
MLA: RSS News
Thorsys
ABSABARES
SensorData
Temperature
Vibration
Soil QualitySensors
Weather
Data Management
System
Sense-T
Confidentiality
Privacy
Ownershipand Control
Longevity
Analytics
Consumers
RetailersProcessors
Producers
Real Time
Apps & Modules
Beef
Animals
Environmental
Supply
Welfare
Quality
Pricing
Customer ExperienceLobsters
Users
Retailers
Producers
Consumers
ShippersProcessors
BOM
Research Stream Outputs
Food Stability Models for bacterial growth
Sensor in supply chains Systems and technologies for improved data transmission throughout value chain
Consumer choice Quantitation of stated consumer preferences for beef purchasing
Natural Capital Accounting Models for valuation of soil natural capital
Environmental sensors Systems for ground/surfacewater sampling and analysis, to underpin NCA stream
Demonstration applications Design of applications for consumer data visualization
Value chains Measurement of attributes of cooperation in value chains
Research Streams
Beef supply chain
Objectives
Present a data-rich story to customers about the product, its provenance, sustainability, safety and quality.
• Increase $ per kg
• Value land stewardship (“Natural Capital Accounting”)
• Enhance quality and productivity
• Drive market access
• Demonstrate innovation
Faculty of Science, Engineering & Technology
Supply Chains, Logistics and Value Generation
Breed Variety
development
Growing
(e.g. LPA)
Fattening
Transport
(e.g.
TruckCare)
Primary
processing
(e.g. AQIS)
Consumption
(end-users)
Marketing &
distribution
Secondary
processing
(e.g. AusQual)
Transport
Raw meat
& by-
products
National Livestock Identification System (NLIS)
Traceability within and between parts of the supply chain
Key Elements of an Extended Australian Premium Beef Supply Chain
On-farm data
Farms• soil and water
quality• weather• grass• animal weight• animal breed
Salmon supply chain
Tasmanian salmon industry
Tasmania provides 95% of salmonid products in Australia
Challenges
• International markets are expanding
• Consumers demand quality fresh product
• Supply chain performance is mostly retrospective
• Growing consumer demand for raw product
Listeria monocytogenes
• Foodborne bacterial pathogen
• Causes listeriosis
• Mortality rate as high as 40% for susceptible individuals http://schaechter.asmblog.org/.a/6a00d8341c5e1453
ef01348647b483970c-800wi
Experimental Design – Spoilage (Microbial)
• Head-on Gutted
• 0 - 15°C
• Total Viable Count (TVC)
Salmo salar(Atlantic salmon)
Experimental Design – Spoilage (Sensory)
Quality Index Metric(QIM)
Experimental Design – L. monocytogenes
• Whole tissues
• 0 - 15°C
• Inoculated with mixture of Lm strains
Results – TVC primary growth curves
Secondary plot of TVC growth rates
√growth rate = 0.0071 x (temperature + 21.86) R2= 0.768
Results – QIM primary curves
Secondary plot of QIM rates
√QIM rate = 0.019 x (temperature + 0.165) R2= 0.919
Relationship betweenQIM and TVC
Secondary plot of Lm growth rates
√growth rate = 0.015 x (temperature + 4.1) R2= 0.995
Oyster supply chain
Vibrio parahaemolyticus
Crassostrea gigas (Pacific oyster)
Vibrio parahaemolyticus
• Causes mild to moderate gastroenteritis
• Responds to climate change
• Market access criteria exist (and more proposed)
• Cold chain management is critical
0.01
0.1
1
10
100
1000
10000
100000
-5 0 5 10 15 20 25 30 35 Water temperature ( C)
V.
pa
rah
ae
mo
lyti
cu
s d
en
sit
y
in o
ys
ter
(Vp
/g)
0.001
0.01
0.1
1
10
100
1000
5 10 15 20 25 30 35
Water salinity (ppt)
resid
uals
of
tem
pera
ture
on
ly r
eg
ressio
n
Model development
• V. parahaemolyticus growth measured from 4 - 30oC• Growth (>15oC) and death rates (<15oC) determined• Models tested (validated) against naturally-occurring Vp
0
1
2
3
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5
6
0 200 400 600
0
1
2
3
4
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6
7
0 50 100 150
V. parahaemolyticus growth rate in live Australian
Pacific Oysters
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
10 15 20 25 30 35
time (hours)
sq
uare
ro
ot
gro
wth
rate
Observed
Predicted
Sydney Rock Oyster
(Saccostrea glomerata)
Pacific Oyster (Crassostrea gigas)
Model validation
Models for V. parahaemolyticus growth and inactivation, and TVC growth
√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
Food Chain IntelligenceKNOWLEDGE...INNOVATION...ACTION
4
6
8
10
12
14
16
18
20
22T
em
pe
ratu
re, °C
-10 0 10 20 30 40 50 60 70Time, hr
Harvest_loc
Storage_farm
transport_truckstorage_domestic
Storage_retail
Transport_domestic
Load Unload
from Madigan 2008
Food Chain IntelligenceKNOWLEDGE...INNOVATION...ACTION
~$23,000 ~$1.6 million
Refrigeration vs Spoilage
Cost Scenarios
Real-time application of predictive models in supply chains
The Holy Grail for predictive microbiologists
Integrated Predictive Models
for Supply Chain Risk Management
