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Shelf life prediction by intelligent RFID -

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1 Shelf life prediction by intelligent RFID -
Dynamics in Logistics 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 Jedermann Walter Lang IMSAS Institute for Microsensors, -actuators and systems MCB Microsystems Center Bremen SFB 637 Autonomous Logistic Processes University of Bremen

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

3 Center for Food Distribution and Retailing

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

5 Strawberries – Case Study
Joint project between Ingersoll- Rand Climate Control and UF 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? Economic impact of monitoring temperature and quality prediction

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

7 Strawberries – Case Study
FEFO = First expires first out = 3 full days = 2 full days 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) = 1 full day = 0 day RFID Temperature Tag + Prediction Models

8 Strawberries – Case Study
Days left Number of pallets Waste random retail Waste (RFID + Model) (Recommendation) 2 91.7% (rejected) (don’t transport) 1 5 53 % (25%) (sell immediately) 8 36.7% (13.3%) (nearby stores) 3 7 10% (10%) (remote stores) Results at the store level (22 pallets sent)

9 Revenue and Profit Strawberries – Case Study Actual RFID + Model
COST $49, $45,480 PROFIT ($2,303) $13,076 Revenue and Profit

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

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

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

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

17 Required Hardware Resources
Type of Resource Calculation of Arrhenius equations Look up table for Arrhenius model Table-Shift Approach Processing time 1.02 ms 0.14 ms 1.2 ms Program memory 868 bytes 408 bytes 1098 bytes RAM memory 58 bytes 122 bytes 428 bytes Energy 6 µJoule 0.8 µJoule 7 µJoule

18 Power consumption per month Typical battery capacities
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 cell 300 … 3000 J Turbo Tag (Zink oxide battery) 80 J

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 Thanks for your attention
The End Thanks for your attention

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