Predictive models to manage food stability in high-value Tasmanian supply chains Mark Tamplin Centre Leader, Food Safety Centre Tasmanian Institute of Agriculture University of Tasmania
Long transport distance = innovation
TIAR Clean Green Important brand Eco-tourism Food tourism Market niche
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
Project goal 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
BOM Thorsys MLA: RSS News External Data Sources ABARES Temperature Sensor Data Soil Quality Sensors ABS MLA: NLIS Vibration Weather Sense-T Confidentiality Data Management System Privacy Ownership and Control Longevity Supply Consumers Processors Retailers Analytics Real Time Apps & Modules Producers Animals Environmental Welfare Pricing Lobsters Customer Experience Quality Beef Retailers Processors Users Producers Consumers Shippers
Research Streams 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
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
Key Elements of an Extended Australian Faculty of Science, Engineering & Technology Premium Beef Supply Chain Supply Chains, Logistics and Value Generation National Livestock Identification System (NLIS) Breed Variety development Growing (e.g. LPA) Fattening Transport (e.g. TruckCare) Primary processing (e.g. AQIS) Traceability within and between parts of the supply chain Consumption (end-users) Marketing & distribution Secondary processing (e.g. AusQual) Transport Raw meat & byproducts
Farms soil and water quality weather grass animal weight animal breed On-farm data
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) R 2 = 0.768
Results QIM primary curves
Secondary plot of QIM rates QIM rate = 0.019 x (temperature + 0.165) R 2 = 0.919
Relationship between QIM and TVC
Secondary plot of Lm growth rates growth rate = 0.015 x (temperature + 4.1) R 2 = 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 100000 1000 V. parahaemolyticus density in oyster (Vp/g) 10000 1000 100 10 1 0.1 residuals of temperature only regression 100 10 1 0.1 0.01 0.01-5 0 5 10 15 20 25 30 35 Water temperature ( C) 0.001 5 10 15 20 25 30 35 Water salinity (ppt)
Model development V. parahaemolyticus growth measured from 4-30 o C Growth (>15 o C) and death rates (<15 o C) determined Models tested (validated) against naturally-occurring Vp 6 5 4 3 2 1 0 0 200 400 600 7 6 5 4 3 2 square root growth rate 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 V. parahaemolyticus growth rate in live Australian Pacific Oysters Observed Predicted 10 15 20 25 30 35 time (hours) 1 0 0 50 100 150
Model validation Sydney Rock Oyster (Saccostrea glomerata) Pacific Oyster (Crassostrea gigas)
Models for V. parahaemolyticus growth and inactivation, and TVC growth Vp growth Vp inactivation TVC growth growth rate = 0.0303 x (temperature - 13.37) R 2 = 0.92 ln inactivation rate = ln 1.81 10-9 + 4131.2 (1/(T+273.15)) R 2 = 0.78 growth rate = 0.0102 x (temperature + 6.71) R 2 = 0.92
22 20 Harvest_loc Transport_domestic 18 Temperature, C 16 14 12 10 8 6 Load Unload transport_truck storage_domestic Storage_farm Storage_retail from Madigan 2008 4-10 0 10 20 30 40 50 60 70 Time, hr Food Chain Intelligence KNOWLEDGE...INNOVATION...ACTION
Refrigeration vs Spoilage Cost Scenarios ~$23,000 ~$1.6 million Food Chain Intelligence KNOWLEDGE...INNOVATION...ACTION
Real-time application of predictive models in supply chains
The Holy Grail for predictive microbiologists
Integrated Predictive Models for Supply Chain Risk Management
Thank you Conference organizers Prof Judith Kreyenschmidt Project partners mark.tamplin@utas.edu.au