Predictive models to manage food stability in high-value Tasmanian supply chains

Transcription

1 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

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3 Long transport distance = innovation

4 TIAR Clean Green Important brand Eco-tourism Food tourism Market niche

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7 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)

8 Transforming Food Industry Futures Through Improved Provenance Sensing and Choice Funded by - Australian Research Council, industry investment

9 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

10 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

11 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

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13 Beef supply chain

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15 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

16 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

17 Farms soil and water quality weather grass animal weight animal breed On-farm data

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19 Salmon supply chain

20 Tasmanian salmon industry Tasmania provides 95% of salmonid products in Australia

21 Challenges International markets are expanding Consumers demand quality fresh product Supply chain performance is mostly retrospective Growing consumer demand for raw product

22 Listeria monocytogenes Foodborne bacterial pathogen Causes listeriosis Mortality rate as high as 40% for susceptible individuals ef b483970c-800wi

23 Experimental Design Spoilage (Microbial) Head-on Gutted 0-15 C Total Viable Count (TVC) Salmo salar(atlantic salmon)

24 Experimental Design Spoilage (Sensory) Quality Index Metric (QIM)

25 Experimental Design L. monocytogenes Whole tissues 0-15 C Inoculated with mixture of Lm strains

26 Results TVC primary growth curves

27 Secondary plot of TVC growth rates growth rate = x (temperature ) R 2 = 0.768

28 Results QIM primary curves

29 Secondary plot of QIM rates QIM rate = x (temperature ) R 2 = 0.919

30 Relationship between QIM and TVC

31 Secondary plot of Lm growth rates growth rate = x (temperature + 4.1) R 2 = 0.995

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33 Oyster supply chain Vibrio parahaemolyticus Crassostrea gigas (Pacific oyster)

34 Vibrio parahaemolyticus Causes mild to moderate gastroenteritis Responds to climate change Market access criteria exist (and more proposed) Cold chain management is critical V. parahaemolyticus density in oyster (Vp/g) residuals of temperature only regression Water temperature ( C) Water salinity (ppt)

35 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 square root growth rate V. parahaemolyticus growth rate in live Australian Pacific Oysters Observed Predicted time (hours)

36 Model validation Sydney Rock Oyster (Saccostrea glomerata) Pacific Oyster (Crassostrea gigas)

37 Models for V. parahaemolyticus growth and inactivation, and TVC growth Vp growth Vp inactivation TVC growth growth rate = x (temperature ) R 2 = 0.92 ln inactivation rate = ln (1/(T )) R 2 = 0.78 growth rate = x (temperature ) R 2 = 0.92

38 22 20 Harvest_loc Transport_domestic 18 Temperature, C Load Unload transport_truck storage_domestic Storage_farm Storage_retail from Madigan Time, hr Food Chain Intelligence KNOWLEDGE...INNOVATION...ACTION

39 Refrigeration vs Spoilage Cost Scenarios ~$23,000 ~$1.6 million Food Chain Intelligence KNOWLEDGE...INNOVATION...ACTION

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42 Real-time application of predictive models in supply chains

43 The Holy Grail for predictive microbiologists

44 Integrated Predictive Models for Supply Chain Risk Management

45 Thank you Conference organizers Prof Judith Kreyenschmidt Project partners