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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IM SASIM SAS

Shelf life prediction by intelligent RFID - Technical limits of model accuracy

Jean-Pierre Emond, Ph.D.Associate Professor, Co-DirectorUF/IFAS Center for Food Distribution and RetailingUniversity of Florida

Reiner Jedermann Walter LangIMSAS Institute for Microsensors, -actuators and systemsMCB Microsystems Center BremenSFB 637 Autonomous Logistic ProcessesUniversity of Bremen

M CBM CB

Dynamics in Logistics

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IM SASIM SASOutline

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

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IM SASIM SASCenter for Food Distribution and Retailing

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IM SASIM SASLaboratory evaluation of shelf life models

Several attributes have to be tested color firmness aroma / taste vitamin C

content

(Nunes, 2003)

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Truck 1 - Front Pallet - Bottom

Wed 07/13 Thu 07/14 Fri 07/15 Sat 07/16 Sun 07/17 Mon 07/18 Tue 07/19

Tem

per

atu

re (

ºC)

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0AirProduct 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

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RFID Temperature Tag + Prediction Models

= 3 full days

= 2 full days= 1 full day= 0 day

0

1

2

3

4

5

6

7

8

9

10

09/29/0517:45

09/30/0505:45

09/30/0517:45

10/01/0505:45

10/01/0517:45

10/02/0505:45

10/02/0517:45

10/03/0505:45

10/03/0517:45Time

Tem

pera

ture

(ºC

)

Air Temperature (ºC) - B Pulp Temperature (ºC) - B Air Temperature (ºC) - C

Pulp Temperature (ºC) - C Air Temperature (ºC) - T Pulp Temperature (ºC) - T

Strawberries – Case Study

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= 3 full days

= 2 full days= 1 full day= 0 day

RFID + Models decision:

2 pallets never left origin2 pallets rejected at arrival5 pallets sent immediately for stores8 pallets sent to nearby stores7 pallets with no special instructions (remote stores)

Strawberries – Case Study

RFID Temperature Tag + Prediction Models

FEFO = First expires first out

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Results at the store level (22 pallets sent)

Strawberries – Case Study

Days left

Number of pallets

Waste random

retail

Waste(RFID + Model)

(Recommendation)

0 2 91.7% (rejected) (don’t transport)

1 5 53 % (25%) (sell immediately)

2 8 36.7% (13.3%) (nearby stores)

3 7 10% (10%) (remote stores)

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Actual RFID + Model

REVENUE $47,573 $58,556COST $49,876 $45,480

PROFIT ($2,303) $13,076

Strawberries – Case Study

Revenue and Profit

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IM SASIM SASThe 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

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IM SASIM SASChain supervision by intelligent RFID

Step 1:

ConfigurationStep 2:

TransportStep 3: Arrival

Step 4:Post control

Full protocolList

• Temperature

• Shelf life

• Transport Info

Handheld Reader

Measures and stores temperature

Calculates shelf life

Sets flag on low quality

Reader gateManufacturer

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IM SASIM SASModeling Approaches

Different model types

0

2

4

6

8

10

0 5 10 15 20

Temperature °C

Sh

elf

life

/ lo

ss in

day

s .

Shelf life(T)

Loss per Day

4.8 days shelf life at 6 °C

Reference temperature 6 °C

Tripple speed of quality decay at 14 °C

Activation energy for Lettuce

1

1

2

3

4

5

0 2 4 6 8Days

Tas

te

0 °C

5 °C

10 °C

15 °C

20 °C

Tables for different temperatures

Reaction kinetic model (Arrhenius)

Differential equation for bio-chemical processesd[P] / dt = −kPPO*[P]d[PPO] / dt = kPPO[P] − kbrown*[PPO]d[Ch] / dt = kbrown*[PPO]

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0 5 10 15 20 250

2

4

6

8

10

12

14

16

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20Color index for Mushrooms

Time in Days

Co

lor

ind

ex(

Sca

led

to 2

0 a

s in

itia

l va

lue

)

4 °C

8 °C12 °C

18 °C

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

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IM SASIM SASModel 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

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IM SASIM SASSimulation

Comparison of reference model (Mushroom DGL) with table shift approach

Parameter tolerances 1 % and 5%

0 2 4 6 8 10 12 14 16 18 200

2

4

6

8

10

12

14

16

18

20

Time in Days

Te

mp

era

ture

°C

an

d c

olo

r in

de

x

Temperature °C

Diff. equation modelTable interpolation R=1%

Table interpolation R=5%

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IM SASIM SASHardware Platforms

Wireless sensor nodes Tmode Sky from Moteiv Own development (ITEM)

Goal Integration into

RFID-Tag Comparable to RFID

data loggers

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IM SASIM SASRequired 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

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IM SASIM SASAvailable 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

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IM SASIM SASSummary 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

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Thanks for your attention

www.intelligentcontainer.com

The End