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1 Shelf life prediction by intelligent RFID - Technical limits of model accuracy Jean-Pierre Emond, Ph.D. Associate Professor, Co-Director UF/IFAS Center.

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Presentation on theme: "1 Shelf life prediction by intelligent RFID - Technical limits of model accuracy Jean-Pierre Emond, Ph.D. Associate Professor, Co-Director UF/IFAS Center."— Presentation transcript:

1 1 Shelf life prediction by intelligent RFID - Technical limits of model accuracy Jean-Pierre Emond, Ph.D. Associate Professor, Co-Director UF/IFAS Center for Food Distribution and Retailing University of Florida Reiner JedermannWalter Lang IMSAS Institute for Microsensors, -actuators and systems MCB Microsystems Center Bremen SFB 637 Autonomous Logistic Processes University of Bremen Dynamics in Logistics

2 2 Outline CFDR / University of Florida Evaluation of quality Case Study Strawberries IMSAS / University Bremen Integration of quality models into embedded hardware Intelligent RFID Feasibility / required hardware resources

3 3 Center for Food Distribution and Retailing

4 4 Laboratory evaluation of shelf life models Several attributes have to be tested color firmness aroma / taste vitamin C content (Nunes, 2003)

5 5 Temperature sensors were placed inside and outside the load at all locations in the trailers Quality was assessed from beginning to end How retailers evaluate the quality of a shipment? Joint project between Ingersoll- Rand Climate Control and UF Economic impact of monitoring temperature and quality prediction Strawberries – Case Study

6 6 RFID Temperature Tag + Prediction Models = 3 full days = 2 full days = 1 full day = 0 day Strawberries – Case Study

7 7 = 3 full days = 2 full days = 1 full day = 0 day RFID + Models decision: 2 pallets never left origin 2 pallets rejected at arrival 5 pallets sent immediately for stores 8 pallets sent to nearby stores 7 pallets with no special instructions (remote stores) Strawberries – Case Study RFID Temperature Tag + Prediction Models FEFO = First expires first out

8 8 Results at the store level (22 pallets sent) Strawberries – Case Study Days left Number of pallets Waste random retail Waste (RFID + Model) (Recommendation) %(rejected)(dont transport) %(25%)(sell immediately) %(13.3%)(nearby stores) 3 710%(10%)(remote stores)

9 9 ActualRFID + Model REVENUE $47,573 $58,556 COST$49,876 $45,480 PROFIT($2,303) $13,076 Strawberries – Case Study Revenue and Profit

10 10 The idea of intelligent RFID Avoid communication bottleneck by pre- processing temperature data inside RFID Temperature curve Function to access effects of temperature onto quality Only state flag transmitted at read out

11 11 Chain supervision by intelligent RFID Step 1: Configuration Step 2: Transport Step 3: Arrival Step 4: Post control Full protocol List Temperature Shelf life Transport Info Handheld Reader Measures and stores temperature Calculates shelf life Sets flag on low quality Reader gate Manufacturer

12 12 Modeling Approaches Different model types Tables for different temperatures Reaction kinetic model (Arrhenius) Differential equation for bio-chemical processes d[P] / dt = k PPO *[P] d[PPO] / dt = k PPO [P] k brown *[PPO] d[Ch] / dt = k brown *[PPO]

13 13 Example Table Shift Approach Only curves for constant temperature are known How to calculate reaction towards dynamic temperature? Interpolate over temperature and current quality to get speed of parameter change Temperature Change from 12 °C to 4 °C

14 14 Model accuracy Measurement tolerances Parameters like firmness or taste have high measurement tolerances Question: Is this table shift approach allowed? Yes, if all entailed chemical processes have the similar activation energies (similar dependency to temperature) Otherwise testing for the specific product required

15 15 Simulation Comparison of reference model (Mushroom DGL) with table shift approach Parameter tolerances 1 % and 5%

16 16 Hardware Platforms Wireless sensor nodes Tmode Sky from Moteiv Own development (ITEM) Goal Integration into RFID-Tag Comparable to RFID data loggers

17 17 Required Hardware Resources Type of Resource Calculation of Arrhenius equations Look up table for Arrhenius model Table-Shift Approach Processing time1.02 ms0.14 ms1.2 ms Program memory 868 bytes408 bytes1098 bytes RAM memory58 bytes122 bytes428 bytes Energy 6 µJoule0.8 µJoule7 µJoule

18 18 Available Energy Power consumption of model is not the issue Multi parameter models are feasible on low power microcontroller Reduce stand by current Power consumption per month Update every 15 minutes (Table shift / 1 Parameter) 20 mJ / month Stand by current of MSP430 (1µA at 2.2V) 5700 mJ / month Typical battery capacities Button cell300 … 3000 J Turbo Tag (Zink oxide battery)80 J

19 19 Summary and Outlook Case study (strawberries) showed the potential to reduce waste and increase profits Quality evaluation of the level of RFID tags is feasible Testing on existing hardware of sensor nodes Development of new UHF hardware required

20 20 Thanks for your attention The End


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